ICP-MS

ICP-MS is one of the widely successful used quantitative multi-element methods which can offer the possibility of a wide detection range of metals in foods with different matrix compositions [15, 18, 19]. Furthermore, ICP-MS is considered the preferred method for detecting heavy metal contents [6].

ICP-MS techniques need several components including a sample introduction system, ion source, interface, ion lens, mass filter, and an ion detector [6]. The principle of this process involves the use of an argon plasma source to dissociate the sample into its constituent atoms or ions [19]. In this stage, the liquid sample is nebulized with an effective nebulizer turning it into a fine aerosol, that is then carried with argon to the ICP torch. Within the plasma, the nebulized water matrix and chemical compounds evaporate, molecules dissociate into atomic constituents, and then ionized into positively single-charged ions. The next step is the extraction of single-charged ions from the argon plasma into the mass analyzers, which can be quadrupole, double-focusing sector field, and time of flight. Extracted ions are separated, in the mass analyzer, based on their mass-to-charge ratio or energy-to-charge ratio, especially in double-focusing SF instruments. The separated ion beams are detected by photomultiplier or Faraday cups. Among the various sample introduction systems developed for ICP-MS, liquid solution nebulization is the most common and cost-effective method [17, 18].

This technique can be used in combination with other analytical methods, for example, LA-ICP-MS, single particle-inductively coupled plasma-mass spectrometry (SP-ICP-MS), liquid chromatography-inductively coupled plasma-mass spectrometry (LC-ICP-MS), to facilitate the sample preparing process for the detection of forms and concentrations of different toxic metals as well as for isotope analysis in different matrix samples [6]. The significant number of ions generated, combined with very low backgrounds, provides excellent detection limits for most metals, typically in the parts per trillion (ppt) ranges [19]. The main benefits of this technique are related to excellent sensitivity, selectivity, accuracy, low detection limits, small sample volume, easy and simple sample preparation, wide linear range, multi-elemental analysis, the possibility of isotopic determination, capable of detecting minute levels, fast, allows for easy control of interferences [9, 15, 17, 18]. However, ICP-MS can have some drawbacks, such as the high cost of the equipment and laboratory setup, the cost of the high-purity gases needed, or the high level of expertise of the operators [9]. Besides this, another very important limit is related to the spectroscopic and non-spectroscopic multi-elemental interferences that can seriously affect its analytical efficiency [15]. This can be prevented by using the non-interfered isotope in case of multi-isotopic metals, by the subtraction of blanks, proper sample preparation, the use of mathematical correction, cold plasma conditions, by the use of collision or reaction cell technology, or by the use of high-resolution mass spectrometers that resolve metals and interferences [18]. Despite its challenges related to atomic and molecular isobaric interferences, multi-elemental interferences, and high cost, ICP-MS has been utilized for the determination of various toxic metals in a range of food items. These include fruits, vegetables, eggs, fish, cereals [20], processed seafood products [21], raw and processed rice, raw and processed chicken [22], and canned beef [23] (Table 1). Elevated levels were observed for As in fruits, vegetables, cereals, fish [20], and canned beef [23], Pb in fruits, vegetables, cereals, and fish [20], and for Cd in fruits, vegetables, cereals, and fish [20]. The concentrations of Cr, Ni, As, Cd, and Pb in all food groups (except chili), exceeded the maximum allowable concentration (MAC) of the tested metals in foods [20]. Additionally, the concentrations of Ad, Cd, Cr, Ni, and Pb, in canned food samples sold in Jordanian markets exceeded the permissible limits set by health organizations such as FAO/WHO [23]. Regarding the performance characteristics of this method, it demonstrated favorable detection and quantification limits for Cd, As, Pb, Sb, Mo, Se, Cr using ICP-MS, being between 0.00006 mg/kg and 0.0584 mg/kg (Table 2) [24–27]. In the case of Cd, the lowest limit of detection (LOD) was obtained by Mohamed et al. [25] and demonstrated for various certified reference materials (CRMs). The obtained regression coefficient of all investigated elements exceeded 0.990. Intermediate precision, as indicated by %RSD by using an internal reference material, ranged from 1.02% [26] to 1.717% [24]. Moreover, the CRMs used revealed improved recovery rates between 89.7 [25] and 108.7 [24]. Giraldo et al. [24] demonstrated that the method maintains accuracy and precision even at very low concentration ranges, between 0.00025–0.010 mg/kg. All these parameters suggest its suitability for the quantification of various toxic and trace metals in food products (Table 2).

ICP-OES

ICP-OES is a multi-element technique spectroscopic technique suitable for the detection of major, minor, and trace metals in different complex samples [19, 28]. The method is based on the spontaneous emission of photons from atoms and ions that have been excited in a radiofrequency discharge. Typically, samples are introduced into the plasma in liquid form. For this reason, solid samples are disintegrated by acid digestion prior to injection. However, gas and liquid samples can be directly injected into the instrument. The next step is a conversion of the sample solution into an aerosol, then sent into the center of the plasma which maintains a high atomization temperature of around 10,000 K. As the plasma generates free atoms in a gaseous state, adequate energy is often available to convert the atoms to ions which are promoted in excited states. The ionic excited state species may subsequently return to the ground state by emitting photons. The specific wavelength of these photons enables the identification of metals. The number of photons is directly proportional to the concentration of the element in the sample. Different sample introduction methods such as electrothermal vaporization (ETV) and laser ablation, as well as, nebulization or hydride generation (HG) which can be used for specific metals including arsenic, selenium, and antimony [19].

This method offers several important advantages represented by the high capacity for the simultaneous metals, its precise detection within short timeframes across wide concentration ranges, and the relatively low detection limits, which are typically lower than those obtained using GF-AAS [28]. This method displays some limitations due to spectral and non-spectral (also known as matrix) interference effects caused by concurrent metals in the sample. The spectral interferences can be corrected using the ICP-OES system software, while non-spectral interferences require optimizing and validating the method. Despite efforts to reduce non-spectral interferences, small matrix effects often persist, leading to inaccurate detection [28].

ICP-OES, as a multi-element method, has been used to evaluate various toxic metals in different food products, such us fruit juices, canned fruits [29], vegetables [30], honey [31], chicken [32], and nuts [28] (Table 1). It was demonstrated that in 97.22% of fruit juice samples, Pb concentration exceeded the Codex limit of 50 μg/kg, while in all canned fruit samples, it remained below the legal limit of the Codex standard (1000 μg/kg) [29]. Additionally, Sn levels in all samples were below the Codex legal limit, with fruit juices at 100 mg/kg and fruit preserves at 250 mg/kg. Hazelnut samples showed the highest Al contamination, especially given that some studies suggest an upper limit of 6 mg per day for Al intake, beyond which toxicity can occur [28]. Alarmingly higher concentrations of As, Pb, Cd, Cr, and Hg were also detected in both tomato and cabbage samples analyzed [30]. Certain Pb concentrations in honey samples exceeded standard levels, while Cd levels were below recommended limits [31]. The highest metal concentrations were obtained in liver samples [32]. Conversely, the lowest levels of Cd, Cu, Fe, Ni, and Zn were detected in meat samples, while gizzard samples showed the highest small amount of Pb. The high levels of heavy metals in the liver can be attributed to their role in the detoxification and storage of heavy metals to substantial levels [32].

The performance parameters of this method are detailed in Table 2. The limits of detection and quantification varied between 0.00005–0.034 mg/kg and 0.00016–0.043 mg/kg, respectively. As shown in Table 2, Karimi et al. [33] achieved regression coefficients between 0.9891 and 0.9899 for Cd and Hg, using a concentration range of 0.0003–1.200 mg/kg. Intermediate precision and precision, expressed as %RSD, using an internal reference material, ranged from 0.961% to 4.104%. Recovery percentages for tested elements by using different CRMs varied between 94.73 [27] and 109.4 [26]. It should be noted that Khan et al. [26] achieved significantly low recovery values for Se. For Cd, the recovery percentage was approximately 92%, slightly lower than those obtained for Cd using ICP-MS with the same reference material [24]. Extended uncertainty demonstrated by Giraldo et al. [24] for Cd analysis was less than 1% for all three concentration ranges (0.001–0.005 mg/kg, 0.010–0.050 mg/kg, and 0.100–0.500 mg/kg). These findings indicate the suitability of this method to quantify the potentially harmful metals in food products.

Atomic absorption spectrometry

Atomic absorption spectrometry (AAS) is one of the earliest commercially developed methods for the elemental analysis of multi-elements (both metals and metalloids) in all types of samples (environmental, biological, industrial, etc.), through the absorption of characteristic spectral lines by atomic vapors generated from a substance [6, 14]. This technique consists of five primary components: a light source, an atomization system, a spectroscopy system, a detection system, and a display unit. The operating principle can be simplified as follows: the atomizer transforms the liquid sample into atomic vapor under high temperatures; atomic vapor irradiated by using a light source, has the capability to absorb radiation at a specific wavelength for each element; the spectroscopic system distinguishes between different spectral lines; the content of the element to be measured in the sample is proportional to the amount of light absorbed [6, 14].

Based on the atomization device used, AAS can be classified into: GF-AAS, F-AAS, CV-AAS, and HG-AAS. Hence, proper digestion techniques are essential for the maximal extraction of specific metals from different samples [6]. Among these techniques, F-AAS and GF-AAS are commonly utilized in many analytical laboratories [9]. Although newer techniques for heavy metal detection have been developed, AAS continues to be a potent tool in analyzing elemental metals in plants and conducting trace analysis, primarily due to the multiple benefits, like high selectivity, accuracy, sensitivity, low interferences, good repeatability, low price, easy to operate, and fastness, which can be used for an extensive range of analyses [6]. Regarding the drawbacks, compared with ICP-OES, ICP-AES, or ICP-MS, AAS technologies can offer single-element analysis, offer a restricted analytical range, involve the use of flammable gases, and require a higher sample volume [9, 14].

X-ray fluorescence

X-ray fluorescence (XRF) is a spectrochemical method used for both identification of elemental composition and quantitative analysis of diverse inorganic materials. The obtained results can be presented as atom percent or weight percent. The needed quantity of sample for chemical analysis depends on the method employed, and the instrumentation, ranging from a few tens of milligrams (approximately 40 mg) to around 12 g [56]. XRF is a physical phenomenon that takes place when high-intensity X-ray radiation generated by an X-ray tube interacts with the sample. Furthermore, this radiation can displace one or more tightly bound electrons from the inner orbitals, leading to the atom becoming unstable. Electrons from outer shells promptly fill the resulting vacancies in the lower orbitals, releasing energy in the form of X-rays. Since the energy levels of electrons vary for each element, the energy of the XRF peak can be linked to a specific element [17, 19]. The energy distribution is measured by an energy-dispersive detector, which can assess the metals present in the sample and their relative concentrations [17]. XRF is an elemental analysis technique that covers o wide range of metals, from sodium to uranium, from various matrices and typically requires minimal sample preparation [19]. XRF spectrometers are commonly used to detect metals with atomic numbers from 4 (beryllium) to 92 (uranium), detecting concentrations ranging from 0.1 µg/g to high percentage levels [17]. The XRF spectrometer includes a radiation (X-ray) source, sample chamber, detector, and computer for data processing [19]. XRF is an advantageous and reliable method used for obtaining detailed elemental information due to its non-destructive nature and continuous readings. The optimized operational parameters cand improve the detection limits and the detection efficiency. Compared with multi-element techniques like ICP-MS/ICP-OES, XRF can offer also, other important advantages, like including minimal sample preparation for solid samples non-destructive analysis, increased overall speed, reduced generation of hazardous waste, and lower operational costs [17].

The sensitivity of XRF depends on factors such as the energy of the incident radiation, instrument geometry, and detector efficiency. Precision in XRF measurements is limited by the nature of detected photons. Also, detection limits are influenced by instrument sensitivity and the background level of the sample matrix. The lack of robustness in calibration methods contributes to substantial systematic errors, that can be significant when the analyzed quantities are very small [18]. Based on the XRF principle, polarized X-ray fluorescence (PXRF), TXRF, or high-definition X-ray fluorescence (HDXRF), synchrotron radiation X-ray fluorescence (SRXRF) was developed. The PXRF and TXRF methods revealed improved peak-to-background ratios. In this case, an electron from the inner orbitals of the target atoms can be rejected after its exposure to photons or charged particles (electrons or ions) with energies higher than the binding energy of the bound inner electrons. TXRF uses radiation that is incident on the tested samples, at an angle below the critical angle, ensuring the complete reflection back [17]. SRXRF is a highly efficient method due to its ability to detect multiple metals, precision, sensitivity, minimal sample preparation requirements, and relatively short analysis time [57]. EDXRF technique was used to evaluate the levels of trace elements in milk and milk products [58, 59], vegetables [60], leafy vegetables [60], pulses [58, 61], eggs, fish [58], cereals [58, 61], and meat [58]. Elevated concentrations were found for Pb in leafy vegetables (0.5–12.3 mg/kg) [60] and for As in leafy vegetables (0.09–1.4 mg/kg) [60] and cereals [61] (Table 1). Additionally, the levels of Fe, Ni, and Cd in both food and plant samples exceeded the WHO/FAO permissible limits, while the concentrations of As, Mo, and Co exceeded the permissible limits only in the plant samples [61]. TXRF and SRXRF techniques were involved in the detection of various toxic and trace metals from rice [62] and coriander leaf and seeds [57]. The highest lead concentration detected in pulses was below the maximum permissible limit [58]. The accumulation of Zn, Mn, Cr, As, and Pb in the studied vegetables exceeded the maximum tolerable levels recommended by the Joint FAO/WHO Expert Committee on Food Additives (1999) [63]. In contrast, the levels of copper, iron, cobalt, and nickel were below the required limits [60]. The findings for Fe, Cu, Zn, and Pb were within the safe limits established by WHO/FAO [61].

Table 2 presents excellent analytical performance in validating the XRF method for detecting various heavy metals. The LODs ranged from 0.00005 mg/kg to 4.383 mg/kg, with corresponding LOQs from 0.00016 mg/kg to 14.611 mg/kg [64, 65]. Calibration curves showed good linearity for all metals (R² > 0.999) using the TXRF method [64], although the linearity was lower for the HDXRF technique [65]. Wang et al. [65] and Beltrán et al. [66] demonstrated excellent accuracy, with percent recoveries ranging from 91% to 108% for real and spiked samples. Nurhain et al. [64] reported lower recovery values between 61.73% and 99.65%. The repeatability and reproducibility of this technique, expressed as RSD, were between 0.13–11.05% and 0.75–6.23%, respectively. These results indicate that the XRF technique is a valuable tool for detecting heavy metals in food products [67].

Stripping voltammetry

Stripping voltammetry (SV) is a useful electroanalytical technique involved in trace metal detection and quantification. This method involves two main steps. In the first step of electrolysis/deposition, the analyte accumulates through faradic processes (anodic or cathodic) or non-faradic processes (adsorptive), at the surface of the working electrode over a specified period. The time of this preconcentration phase depends on the concentration of the analyte in the solution. In this way, lower element concentrations require longer accumulation times until a sufficient amount of sample is on the electrode surface. Accumulation increases with time rather than with the concentration of the element in the sample, allowing extremely low detection limits to be achieved. The first preconcentration phase is followed by a stripping step, in which the previously accumulated metal is released into the solution by applying an anodic potential. The resulting current during the stripping process is directly proportional to the metal concentration in the water sample. In the electrolysis step, the analyte of interest and the other metals that can be reduced at this deposition potential are reduced at the working electrode. When a low deposition potential is used under acidic conditions, hydrogen is generated at the surface of the working electrode by proton reduction, while oxidants are produced at the auxiliary electrode. Hydrogen production presents challenges because it can block the electrode surface, compromise reproducibility, and increase the noise level of voltammograms, but the new equipment contains a rotating electrode to avoid the problem of hydrogen bubble obstruction [18]. The advantages of this technique include remarkably low detection limits, high sensitivity, and the capability to detect trace metals in different oxidation states. Additionally, the portability of instrumentation, rapid analysis capabilities, and relatively low costs for basic instrumentation and operation should also be mentioned [18, 19]. SV also facilitates the analysis of speciation, because the deposition of different oxidation states of a given element usually occurs at distinct potentials [67]. As concerning the limitations of this technique, one issue is related to the presence of its toxic soluble salts released by the mercury film electrode [18]. Besides this, thin film mercury electrodes may provide lower LOD [19]. For this reason, nonmercurial electrodes like electropolymerized polymer film, boron-doped diamond, bismuth, antimony, as well as silver or gold electrodes can be used as alternatives to the mercury electrode. The detection of multiple metals by stripping analysis poses challenges due to overlapping potential peaks in the narrow potential range where metals undergo reduction or oxidation processes. However, there are methods for the simultaneous detection of arsenic, copper, lead, and mercury in the presence of oxygen using differential pulse ASV with a vibrating gold microwire electrode, allowing analysis without the need for deoxygenation. This aspect simplified the stripping process and reduced the measurement time. Under acidic conditions, the applicability of SV is constrained at relatively high deposition potentials because of the interfering effects of the hydrogen generation at the working electrode [18].

Depending on the nature and direction of the preconcentration and stripping phases, voltammetry can be anodic, cathodic, and adsorptive SV. Besides these, other less common stripping methodologies are used which include potentiometric stripping analysis (which, although not a voltametric technique, is based on similar deposition mechanisms as SV) and abrasive SV [68]. Concerning SV methodologies, ASV and CSV were employed for the analysis of diverse harmful and trace metals in cheese, yogurt [13], and rice [69]. The findings of these studies revealed decreased concentrations of the examined metals. Most of the metal concentrations analyzed were below internationally accepted limits, indicating no health concern [13].

Table 2 highlights the excellent analytical performance of the ASV and CSV methods for detecting various heavy metals. In the case of ASV methods, the LODs ranged from 0.00005 mg/kg to 0.02512 mg/kg, with corresponding LOQs from 0.0001 mg/kg to 0.500 mg/kg. Calibration curves exhibited good linearity for all metals (R² > 0.990) [70], except the results obtained by Palisoc et al. [71, 72], which obtained coefficients of regression between 0.9367 and 0.9714. As concerning the accuracy of the method, Shahbazi et al. [13], demonstrated excellent percent recoveries for Zn, Cd, Pb, and Cu ranging from 95% to 98%. The obtained results demonstrated the suitability of this method for the quantification of potentially toxic metals [13]. Regarding the CSV method, Shahbazi et al. [13] demonstrated enhanced performance parameters for the detection of Se. The LOD and LOQ were 0.00014 mg/kg and 0.0004 mg/kg, respectively, within a linear range of 0.0004 mg/kg to 0.120 mg/kg. Also, the method also proved its accuracy with a recovery rate of 91% [13]. Due to the complex matrix of the samples, it is evident that no single analytical technique can be employed to determine all metals present in the sample. One of the primary criteria for selecting an analytical method is related to the LOD, because AAS and XRF methods enable determination at the ppm level, ICP-OES and GF-AAS permit analysis at the ppb level, and only ICP-MS can detect very low concentrations in the ppt range. Another important criterion is the capability of the technique to analyze multiple metals simultaneously. ICP-MS, ICP-AES, and ICP-OES are multielement techniques, compared with AAS, which is limited in terms of multielement analysis, but is cost-effective and requires less maintenance compared to other techniques. Besides this, attention must be given to non-spectral and spectral interferences in measurements conducted by techniques such as ICP-AES, ICP-MS, and AAS. For this reason, the samples must be subjected to digestion or at least dilution processes to reduce the presence of interfering compounds before analysis [17].

Sample digestion is a critical step in elemental analysis due to the risk of contamination and analyte loss, which can lead to systematic errors. Traditional dry ashing and wet digestion are commonly used procedures for organic matter digestion in samples. Effective sample digestion procedures can reduce the chances of significant loss or contamination. Dry ashing methods often result in significant loss or contamination and are therefore unsuitable for analyzing minor and trace elements like Mo, Se, and Cr. In contrast, wet digestion methods, such as using a heating block or microwave, yield good recoveries and are considered effective for sample preparation [26]. Errors during sample preparation can be minimized by using appropriate ashing methods, proper digestion acids, and high-purity acids [17].

Regarding wet digestion, the results presented by Helal Uddin et al. [73], revealed that using HNO₃ and HCl in a 1:3 ratio was the most efficient digestion method. It provided significantly higher recoveries (P < 0.05) for all metals compared to using HNO₃ alone or HNO₃ and HClO₄ in a 2:1 ratio. The recovery values obtained by Akinyele and Shokunbi [74] from both dry-ashed and wet-digested samples were nearly quantitative (> 90%), except for chromium analysis, where recoveries were around 80%. In most spiked samples, the obtained results demonstrated that dry-ashed samples had slightly higher recovery rates. Based on these results, the authors consider the dry ashing method can be more sensitive than the wet digestion method. Additionally, they recommend the dry ashing method for four reasons: it is cost-effective, involves fewer risks associated with chemical usage, requires simple equipment (muffle furnace) that is easy to handle, and achieves better recovery in the samples [74].

Yang et al. [75] demonstrated that microwave-assisted digestion yielded improved recovery values for CRMs compared to dry ashing and wet digestion methods, due to the several advantages over conventional wet ashing and dry ashing procedures, which were more time-consuming and complicated without providing any additional benefits in digestion efficiency. The microwave digestion procedure is simpler, more effective, faster, and less prone to contamination [75]. As shown in Table 2, microwave-assisted digestion was the most commonly used technique across all analytical methods, likely due to its numerous advantages.

The levels of heavy metals in food products are affected by various factors, such as the type and variety of plants, their bioavailability, cultivation methods, environmental conditions, and their ability to bioaccumulate [9]. In addition, post-harvest practices, including storage, packaging, and cooking techniques, play a significant role. Practices such as washing after harvest usually remove metal contaminants, while cooking can either decrease or increase metal content [76]. The high concentrations of As, Pb, Cd, Cr, and Hg detected in both tomato and cabbage samples were associated with high levels of these metals in agricultural soil [30].

Contamination observed in fruits can be correlated with soil and water contamination during growth, water use during processing, equipment safety failures, and potential migration of heavy metals from packaging [29]. Similarly, increased levels of toxic metals found in milk and milk products could be attributed to increased soil and water exposure to lead sources near hazardous waste sites [39]. In addition, the geographical region of the tested samples affects the concentrations of harmful or trace metals. Certain metals showed higher concentrations in the Eastern region due to the presence of more industries there [31]. Similarly, heavy metal content in rural samples was high compared to urban samples [4]. Also, the highest levels of mercury were detected in imported dried apples compared to those from local sources [52].

Moreover, the information provided in Table 1 highlights the importance of monitoring food composition and establishing regulations and thresholds for contaminants in globally traded foods. This effort aims to ensure food safety and promote a healthy diet [42].