Metal profiles and health risk assessment of the most consumed rice varieties in Spain

Journal of Food Composition and Analysis 117 (2023) 105101 Available online 20 December 2022 0889-1575/© 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/). Metal profiles and health risk assessment of the most consumed rice varieties in Spain Jos´ e Raúl Aguilera-Vel´ azquez a , Ana Calleja b , Isabel Moreno c , * , Juan Bautista a , Esteban Alonso d a Department of Biochemistry and Molecular Biology, University of Sevilla, C/Profesor García Gonz´ alez, 2, 41012 Seville, Spain b Centro de Investigaci´ on, Tecnología e Innovaci´ on (CITIUS), University of Sevilla, Avenida Reina Mercedes 4B, 41012 Seville, Spain c Area of Toxicology. Department of Nutrition and Bromatology, Toxicology and Legal Medicine, University of Sevilla, C/Profesor García Gonz´ alez, 2, 41012 Seville, Spain d Departamento de Química Analítica, Escuela Polit´ ecnica Superior, Universidad de Sevilla, C/Virgen de ´ Africa, 7, 41011 Sevilla, Spain ARTICLE INFO Keywords: Metals Metalloids In vitro digestion ICP-MS Long rice Short rice Bioavailability Target Hazard Quotient Incremental lifetime cancer risk ABSTRACT There are numerous publications describing the mineral content of row rice and its impact on human health. However, there are scarce studies about the bioaccessibility and health risk assessment of metallic and metalloid elements from cooked and digested rice. Thus, the aims of the present study were i) to determine the concentration of toxic metals (Al, As, Cd, Pb) and trace and essential elements (Ni, Se, Cr, Cu, Mn and Zn) in cooked and digested rice; ii) to evaluate the adult daily intake and iii) to estimate the non-carcinogenic and carcinogenic human risk related to cooked and digested rice consumption. The bioavailability of the elements studied and their contribution to human health risk was obtained by an in vitro digestion method simulating the human digestion. The results obtained evidenced that Pb had the lowest bioavailability, in contrast with As, which had the highest. Concerning the non-carcinogenic risk test, all values obtained in cooked and digested rice were below the toxic threshold. Furthermore, Pb was the smallest contributor to carcinogenic risk, while Cr-values are the closest to the limit (10 -4 ). Thus, it could be stated that human metallic exposure through rice consumption is overestimated when metals are measured in row rice. 1. Introduction Nowadays, food safety has become a major concern worldwide. A wide range of diseases is directly linked to the presence of contaminants in food. Environmental contaminants such as dioxins, polychlorinated biphenyls (PCBs), heavy metals and metalloids, phthalates, and bisphenol A are under strict supervision to avoid their presence in the food chain (Kamala and Kumar, 2018). Both acute and chronic exposure to metallic elements can lead to toxic effects, depending on the time and concentration of exposure (Rajkumar and Gupta, 2021). Some metallic elements such as cadmium (Cd), lead (Pb) or chromium (Cr), and metalloids such as arsenic (As) can represent a serious public concern in animal and human health (Engwa et al., 2019; Tchounwou et al., 2012). Effects on the digestive, renal, nervous, immune, and cardiovascular dysfunction, skin disorders, teratogenic effects, or cancer are some examples of toxicity caused by the exposure to these elements (Dastgiri et al., 2010; Martinez et al., 2011; Ni˜ no et al., 2019; Rahman et al., 2009; Singh et al., 2011; Tchounwou et al., 2012). In this regard, continued exposure to metals such as cobalt (Co) and aluminum (Al) is mainly related to neurotoxic effects such as Alzheimer’s and Parkinson’s diseases (Catalani et al., 2012; Exley, 2016; Igbokwe et al., 2019; Simonsen et al., 2012; Skalny et al., 2021). Moreover, essential metals such as copper (Cu), selenium (Se), or manganese (Mn) after exposure to concentrations above the functional threshold can generate neurotoxicity, cardiovascular toxicity, hepatotoxicity, or even infant mortality (Hadrup and Ravn-Haren, 2020; MacFarquhar et al., 2010; O’Neal and Zheng, 2015; Peres et al., 2016; Tapiero et al., 2003). Metal contamination can have a natural or anthropogenic origin, and it is characterized by its persistence in soil. Ash from volcanoes or forest fires, waters contaminated by the uncontrolled use of agrochemical fertilisers, or industrial waste, among others, lead to the accumulation of metals in the environment and thus their entrance in the food chain. Although metallic and metalloid elements can be bioaccumulated after their inhalation or through dermal contact, 90% of the bioaccumulated metals came from their intake (Bas¸aran, 2022a; Zhang et al., 2019). Rice (Oryza sativa L.) is a staple food for more than half of the world’s * Correspondence to: Area of Toxicology, Faculty of Pharmacy, University of Seville, Profesor García Gonz´ alez, 2, 41012 Seville, Spain. Contents lists available at ScienceDirect Journal of Food Composition and Analysis journal homepage: www.elsevier.com/locate/jfca https://doi.org/10.1016/j.jfca.2022.105101 Received 26 April 2022; Received in revised form 14 November 2022; Accepted 19 December 2022 Journal of Food Composition and Analysis 117 (2023) 105101 2 population, with an annual production in 2018 of 510 million tons (Olayiwola et al., 2020). Furthermore, for a high percentage of the population, it is the only source of protein and carbohydrates. Rice, as other cereals, has a high capacity to accumulate metallic elements (Zulkafflee et al., 2021), which could lead to a risk for human health. These metallic elements are bioaccumulated in the edible parts of rice plants and, thus, reach the trophic chain. Due to high rice consumption and increased concern about food safety, it is vital to perform exhaustive controls to evaluate the concentration of metallic elements. The bioaccessible form of metallic and metalloid elements is defined as “the fraction of total metallic and metalloid elements ingested that is available to be absorbed by the digestive tract”. Determination of bioaccessible forms of metallic and metalloid elements can be performed through in vitro digestion models (Brodkorb et al., 2019). Bioaccessibility determination has the aim of simulating a realistic physiological scenario, since the effect of actual human health risk bioaccumulation of metals is overestimated (Versantvoort et al., 2005). Publications describing the mineral content of rice in Spain and its impact on human health are scarce. Some similar studies with other cereals can be found (Rubio-Armend´ ariz et al., 2021), although there are no bioaccessibility and health risk assessment studies of metallic and metalloid elements from rice in Spain. Only some similar bioaccessibility studies in rice have been carried out in other countries (Omar et al., 2013; Praveena and Omar, 2017). As far as we know the present study is the first in Spain to analyse the bioaccessibility of fourteen of the most consumed rice varieties in this country in order to get a better understanding of the impact of the accumulation of metals from rice on human health. Taking this into account, the main goals of the present study were: i) to determine the concentration of ten metallic and metalloid elements (Al, As, Cd, Cr, Cu, Mn, Ni, Pb, Se, and Zn) in cooked and digested rice; ii) to evaluate the adult daily intake and iii) to estimate the non-carcinogenic and carcinogenic risk to human health related to cooked and digested rice consumption. 2. Materials and methods 2.1. Chemicals, reagents and standards All reagents used were of analytical grade or higher, and were purchased from Panreac (Barcelona, Spain) and Sigma-Aldrich (Madrid, Espa˜ na). Type I water (>18 MΩ cm) was obtained from an Elix-Milli-Q water purification system (Millipore, Bedford, MA, USA). All labware used was made of plastic and cleaned by soaking at least 5 h in 20% (v/ v) HNO 3 . Nitric acid was obtained in-house by double distillation in the DST-1000 sub-boiling system purchased from Savillex (Eden Prairie, MN, USA). Following the directions stated in EPA method 200.8 (EPA. 200), labware was rinsed three times with type I water and dried in a laminar flow hood. Reagent blank consisted of 1% (v/v) HNO 3 . ICP multielement calibration standard solution ICP-MS-200.8-CAL1–1 was purchased from AccuStandard (New Haven, CT, USA) and diluted as needed to obtain a working range of 0–250 mg/g. The digestive enzymes, i.e. pepsin (porcine, 367 units per mg solid, measured as TCA- soluble products using haemoglobin as a substrate) and pancreatin (porcine, 8 ×USP specifications of amylase, lipase, and protease), were also purchased from Sigma-Aldrich. Salts (CaCl 2 (H 2 O) 2 , NaOH, HCl, KCl, KH 2, PO 4 , NaHCO 3 , NaCl, MgCl 2 ,(H 2 O) 6, (NH 4 )CO 3 , CaCl(H 2 O) 2, and Tris-HCl buffer) were obtained from Sigma-Aldrich. 2.2. Sampling and study design A total of 42 samples (triplicate of each of the 14 varieties) of the most widely marketed and consumed rice varieties in Spain were analyzed. The samples were collected from the most popular Spanish supermarkets, taking samples of all the brands, including those commercialized by the supermarket under their own brand. When the brand had more than one type of grain, all types were collected. The samples were collected in triplicate, taking the same brand for the triplicate and ensuring the same batch whenever possible, noting the identification data of each sample (origin, type of grain, concentration of starch, year of collection, color of the grain and aromaticity) (Table 1). To remove the metallic environmental contamination, all rice samples were washed twice with deionised water and dried at 65 ºC for 48 h in a furnace. In all cases, the assays were carried out in triplicate. 2.3. Cooking process One triplicate of samples of each rice variety was cooked before being subjected to the in vitro digestion process to calculate the amount of metallic and metalloid elements absorbed in the digestive tract. Regarding the cooking process, 100 g of each sample was boiled, at a ratio of 1:2 (v/w) for 15 min and slowly cooled. All cooked samples were dried at 65 ºC for 48 h in a furnace to obtain a constant weight. Samples were kept in a desiccator until the following step. 2.4. In vitro digestion process Five grams of each cooked rice sample were digested following a standard in vitro digestion method previously described by Brodkorb et al. (2019). Briefly, the method consists of an in vitro simulation of human digestion process, which is subdivided into three stages or phases. i) Oral phase, in which the mastication process was simulated in a ceramic mortar to avoid the presence of metallic elements. The simulated salivary fluid (SSF) electrolyte stock solution (pH 7) was added to rice in a final proportion of 50/50 (w/v). Human salivary-amylase (Enzyne Comission (EC) number: 3.2.1.1) and CaCl 2 were combined with SSF to obtain a final concentration of 75 U/mL and 0.75 U/mL, respectively. Samples were incubated in a Q5000 shaker (Thermo Fisher Scientific Inc. Waltham, MA, USA) at 37 ºC and 95 rpm for 2 min. ii) Gastric phase, where the stock electrolyte solution of simulated gastric fluid (SGF) (pH 3) is combined with five parts of the oral bolus until a 50/50 bolus / SGF ratio is obtained (w/v). Porcine pepsin (EC 3.4.23.1) and CaCl 2 were added to obtain 2000 U/mL and 0.075 nM in the final concentration. Samples were incubated in a Max Q5000 shaker at 37 ◦C and 95 rpm for 2 h. iii) Intestinal phase, where five parts of gastric chime were mixed with four parts of the stock electrolyte solution of the Simulate Intestinal Fluid (SIF) (pH 7) in a final gastric chime/SIF relationship of 50/50 (w/v). In this case, CaCl 2 was added until a final concentration of 0.3 mM was achieved. Furthermore, a mixture of bile extract and pancreatin (trypsin activity 100 U/mL) was added to the gastric chime/SIF combination. The samples were incubated in a Max Q5000 shaker at 37 ◦C and 100 rpm, for 2 h. After the digestive process, whole samples were centrifuged at 30,000 g (Eppendorf 5430/ 5430 R) for 2 hours to obtain an aqueous micellar phase, in which the absorbable metallic elements are found. The composition of each electrolyte solution from each simulated fluid stock is detailed in our previous work Aguilera-Vel´ azquez et al. (2021). 2.5. Bioavailability The estimation of the fraction of an ingested biocomponent (metallic and metalloid elements in our case) that becomes accessible for absorption through the epithelial layer of the gastrointestinal tract (GIT) or bioavailability was estimated using Eq. (1). BioaccessibilityMetals =mgmetalindigestiblefraction mgmetalincookedrice X100 (1) 2.6. Metallic elements determination 2.6.1. Sample treatment One gram of each sample, cooked and digested rice, was placed in a pressure vessel previously decontaminated. Then, 5 mL HNO 3 65% J.R. Aguilera-Vel´ azquez et al. Journal of Food Composition and Analysis 117 (2023) 105101 3 +5 mL H 2 O +1 mL H 2 O 2 30% were added to each vessel, and the digestion program was run (Table S1). (Aguilera-Vel´ azquez et al., 2021). 2.6.2. Analytical methods Multielemental analysis was carried out by ICP-MS (model 8800 ICPMS from Agilent Technologies). Some of the most relevant operating conditions of ICP-MS, such as instrumental parameters, LOD and LOQ values, or the isotopes analysed for each element are indicated in Tables S2-S7. Also, calibration curves are included as Figs. S1–S10. 2.7. Human health risk assessment The human health risk associated with the accumulation of metallic and metalloid elements can be divided into two categories, noncarcinogenic and carcinogenic health risks (Basaran, 2022). 2.7.1. Calculation of daily intake Estimated daily rice intake (EDI) was calculated according to Eq. (2) shown below, EDI =(EFxEDxFIRxMC) (BWxAT).(2) where EF is the exposure frequency (in this case 365 days/year), ED is the duration of the exposition for a Spanish adult (54 years), RF is the food ingestion rate (10 g of rice/day per capita) (Ministerio de Agricultura, 2018). The MC value is the metal concentration found in rice (mg/g dry weight), BW would correspond to average Spanish body weight (75 kg) (Evers and Hughes, 2002). Moreover, AT is the averaging time for both effects: non-carcinogenic risk (54 years x 365 days) and carcinogenic risk (70 years x 365 days) (Sharafi et al., 2019b). 2.7.2. Non-carcinogenic risk assessment To estimate the non-carcinogenic risk associated with metallic element intake, the Target Hazard Quotient (THQ) was calculated according to Eq. (3) (Bas¸aran, 2022b; Chien et al., 2002). THQ =EDI Rfd .(3) In this equation, RfD is the Oral Reference Dose expressed in (mg/kg day), which values used in this study are listed in Table S8 (USEPA, 2016). As EPA has not derived a reference dose (RfD) or reference concentrations (RfC) for Pb or total Cr, thus, THQ of these elements were not calculated. Total Target Hazard Quotient (TTHQ) was used to assess the noncarcinogenic risk associated with the entire group of metallic elements. It is expressed as the sum of the individual THQs of each metal and is calculated according to Eq. (4). If the THQ or TTHQ values were less than one, the non-carcinogenic risk associated with accumulated metallic elements would be considered below the chronic toxicity threshold. However, if one or more of these factors were higher than one, the non-carcinogenic risk would be considered as unacceptable. TTHQ =ƩTHQmetallicelements (4) 2.7.3. Carcinogenic risk assessment The incremental lifetime cancer risk (ILCR) method was used to evaluate the carcinogenic risk associated with long-term exposure of metallic elements, following Eq. (5). ILCR =EDIXOSF (5) From this equation, OFS is the cancer slope factor, which is understood as the risk of developing cancer throughout life by exposure to carcinogenic elements at a concentration of 1 mg/kg of body weight per day. The OFS values of the carcinogenic element are described in Table S9. As EPA has not derived an OFS for Pb, Cd or total Cr, the ILCR of these elements were not calculated. In the rest of the cases, ILCR>10 - 6 represents very low or acceptable risk, ILCR: 10 -6 −10 -5 represents low risk, ILCR: 10 -5 -10 -4 means medium risk, ILCR: 10 -4 -10 -3 corresponding to a high risk and ILCR: >10 -3 poses a very high risk. 2.8. Statistical analysis Each data was expressed as the mean ±standard deviation (SD). The normality of continuous variables in groups was determined by the Shapiro–Wilk test. Results were statistically analysed using Analysis of Variance (ANOVA) tests; a value of P≤0.05 was considered statistically significant. Data were analysed using SPSS for Windows (24.0). 3. Results and discussion 3.1. Metallic elements concentration and bioavailability The mean concentration of metallic and metalloid elements expressed in mg/kg and the standard deviation (SD) of the cooked and digested rice samples are shown in Tables 2 and 3, respectively. Taking into account that there are only a few studies about metallic levels in cooked and digested rice, our results will be also compared with those that detected metals in raw rice or cereals. In general, all data obtained are within the same order of magnitude as the data provided by other researchers in raw, cooked, or digested rice (Kukusamude et al., 2021; Sharafi et al., 2019a; Zulkafflee et al., 2019). Specifically, the toxic metals analyzed followed the descending order of Al >Cr ≥As >Ni >Pb >Cd, both in cooked and digested rice, similar to those Table 1 Origin and type of grain of the rice samples analyzed. Rice varieties Origin Type of grain Concentration of starch Collecting samples Colour Aromatic Barone Greek Short High 2020 White No Guadiamar Spain Short High 2020 White No Hispamar Spain Short High 2020 White No Marisma Spain Short High 2020 White No Perlado Spain Short High 2020 White No Pi˜ nana Spain Short Low 2020 White No Sendra Spain Short High 2020 White No Sole Italy Short High 2021 White No Barone Greek Short High 2020 White No Basmati India Long Low 2021 White Yes Jasmine Vietnam Long Low 2020 White Yes Puntal Spain Long Low 2020 White Yes Memby Argentino Long Low 2021 White Yes Sona Masoori Indio Long Low 2021 White Yes Thaiperla Spain Long Low 2021 White Yes *Long grain, 6.61–7.50 mm; short grain <6.61 mm (International Rice Research Institute, IRRI). Low starch content, 10–20%; Intermediate starch content, 20–25%, High starch content, >25% (Chatterjee, L. and Das, P., (2018)). J.R. Aguilera-Vel´ azquez et al. Journal of Food Composition and Analysis 117 (2023) 105101 4 obtained by Rubio-Armend´ ariz et al. (2021) and Brizio et al. (2016) in cereals from Canary Islands (Spain) and Italy, respectively. In the present study, Al is the toxic metal that reached the maximum value in Guadiamar variety from Spain (37 ±1 mg/kg in cooked and 11 ±3 mg/kg in digested samples). However, despite the toxicological considerations of this neurotoxic element, current European legislation does not include maximum levels of Al in food. Furthermore, the highest concentration of Pb (0.68 ±0.02 mg/kg in cooked rice and 0.19 ±0.01 mg/kg in digested one) was detected also in Guadiamar variety. However, the highest As concentration was detected in Basmati rice from India (6.00 ±0.12 mg/kg in cooked rice and 4.10 ±0.01 mg/kg in digested one), which is in agreement with some studies that showed As as the main contaminant in Indian soils (Khandare et al., 2021). These results agree with those obtained by Zulkafflee et al., (2019) who detected a range of 0.20–0.50 mg/kg and 0.19–0.54 mg/kg of Pb and As, respectively, in digested rice. However, heavy metal concentrations detected in 22 varieties of rice cooked and digested from Malaysia were lower than those detected in the present study. In Malaysian rice, As was the metalloid detected in lower quantities (around 0.09 mg/kg in cooked rice and 0.02 mg/kg in digested ones), while Al and Cr were the toxic metal detected at the highest concentrations, although in lower levels than in the present study (0.14–17 mg/kg and 0.60–9.10 mg/kg, respectively in cooked rice, and around 0.88 mg/kg and 0.10 mg/kg, respectively, in digested ones) (Praveena and Omar, 2017). The statistical analysis did not show significant differences (P>0.05) in the concentrations of Al, Cr, Mn, and Se between all cooked rice in our studynvironment. However, significant differences in levels of Cu, Zn, As, Cd, and Pb within the varieties employed were detected. These differences could be due to the ability of each rice variety to bioaccumulate these metals in combination with their concentration in the croplands. In digested rice, there are significant differences in all metals except Pb due to the different digestibility degree in each rice variety. However, when comparing metallic concentration between short and long-grain rice, the values are very similar and show no significant differences except Al. Short-grain rice presented a hundred-fold higher Al-level (100⋅10 -4 ±93⋅10 -4 mg/kg) than the long grain once (1.1⋅10 -4 ±17⋅10 -4 mg/kg) (Table 4). Each rice variety shows a different degree of digestibility, leading to a different degree of bioavailability of the metallic and metalloid elements is shown in Table 5. Bioavailability was calculated as the percentage of metals found in the absorbed phase (in vitro digestion methods) in relation to the metals measured in the cooked rice. The mean values of the bioavailability of the metallic elements of all varieties studied decrease in the following order: As>Al>Ni>Mn>Cu>Cd>Zn>Cr>Se>Pb. Inorganic arsenic was the Table 2 Concentration of metallic elements in cooked rice. Results are expressed as mean±standard deviation (mg/kg) (n =3). Rice varieties Al Cr Mn Ni Cu Zn As Se Cd Pb Barone 16.00 ±0.10 a 0.44 ±0.01 a 47.00 ±0.44 ab 0.28 ±0.16 ab 12.00 ±0.10 cd 130.00 ±0.00 abc 0.73 ±0.01 ab 0.42 ±0.01 b 0.12 ±0.00 cd 0.19 ±0.00 c Basmati 8.10 ±0.15 a 0.27 ±0.01 a 65.00 ±1.50 d 0.24 ±0.02 e 97.00 ±0.07 de 79.00 ±0.89 d 6.00 ±0.12 f 0.26 ±0.01 a 0.03 ±0.00 cd 0.10 ±0.00 bc Guadiamar 37.00 ±1.00 b 0.84 ±0.04 a 58.00 ±2.10 a 0.30 ±0.24 bc 19.00 ±1.00 ab 190.00 ±7.00 ab 0.30 ±0.01 a 0.56 ±0.00 a 0.05 ±0.00 a 0.68 ±0.02 abc Hispamar 8.00 ±0.17 a 0.35 ±0.02 a 33.00 ±0.15 bc 0.14 ±0.01 ab 7.50 ±0.33 bcd 45.00 ±1.70 ab 0.14 ±0.01 a 0.21 ±0.01 a 0.03 ±0.00 ab 0.07 ±0.00 ab Jasmine 11.00 ±0.19 a 0.53 ±0.02 a 22.00 ±0.21 ab 0.34 ±0.01 cd 6.60 ±0.10 abc 86.00 ±1.00 bc 0.24 ±0.00 a 0.30 ±0.01 ab 0.05 ±0.00 c 0.11 ±0.00 ab Marisma 8.40 ±0.14 a 0.35 ±0.01 a 33.00 ±0.31 ab 0.15 ±0.00 abc 11.00 ±0.14 e 61.00 ±0.47 abc 0.14 ±0.00 a 0.22 ±0.00 a 0.02 ±0.00 a 0.07 ±0.00 a Memby 11.00 ±0.01 a 0.57 ±0.00 a 66.00 ±0.63 d 0.90 ±0.01 f 12.00 ±0.19 de 10.00 ±0.63 abc 1.10 ±0.01 bc 0.33 ±0.01 a 0.04 ±0.00 de 0.13 ±0.00 ab Perlado 14.00 ±0.23 a 0.73 ±0.02 a 0.04 ±0.00 ab 0.00 ±0.00 a 7.90 ±0.07 abc 91.00 ±0.91 ab 0.20 ±0.01 a 0.38 ±0.01 a 0.02 ±0.00 ab 0.15 ±0.00 ab Pi˜ nana 8.20 ±0.14 a 0.62 ±0.01 b 30.00 ±0.71 ab 0.48 ±0.01 e 5.60 ±0.09 ab 49.00 ±0.43 a 0.14 ±0.00 a 0.26 ±0.06 a 0.02 ±0.00 a 0.08 ±0.00 ab Puntal 7.30 ±0.15 a 0.31 ±0.00 a 28.00 ±0.30 ab 0.10 ±0.00 abc 7.40 ±0.08 cd 52.00 ±0.15 ab 0.16 ±0.00 a 0.27 ±0.01 a 0.02 ±0.00 b 0.08 ±0.00 abc Sendra 6.90 ±0.15 a 0.29 ±0.01 A 51.00 ±1.50 cd 0.15 ±0.00 ab 8.50 ±0.10 bcd 69.00 ±1.50 abc 0.16 ±0.00 a 0.27 ±0.01 a 0.02 ±0.00 ab 0.07 ±0.00 bc Sole 1.00 ±0.17 a 0.61 ±0.00 a 38.00 ±0.41 bc 0.74 ±0.02 ab 4.60 ±0.08 a 0.08 ±0.00 abc 0.40 ±0.00 ab 0.26 ±0.01 a 0.07 ±0.00 f 0.10 ±0.00 ab Sona Masoori 1.00 ±0.60 a 0.54 ±0.02 a 0.02 ±0.00 ab 0.30 ±0.00 d 8.80 ±0.60 de 0.07 ±0.00 abc 0.20 ±0.00 a 0.46 ±0.02 c 0.09 ±0.00 f 0.09 ±0.00 ab Thaiperla 0.01 ±0.00 a 0.29 ±0.01 a 46.00 ±0.27 abc 0.26 ±0.03 a 6.90 ±0.13 acb 0.08 ±0.00 abc 0.13 ±0.09 c 0.35 ±0.00 a 0.04 ±0.00 e 0.13 ±0.00 abc Average 10.00 0.47 44.10 0.36 15.00 77.60 0.26 0.35 0.04 0.14 Standard desviation 8.70 0.20 15.00 0.25 23.00 42.20 0.20 0.09 0.03 0.15 Different letters in the same columns indicate significant differences by ANOVA test (p ≤0.05). J.R. Aguilera-Vel´ azquez et al. Journal of Food Composition and Analysis 117 (2023) 105101 5 element with the highest bioavailability (79.09%) in contrast to the bioavailability of Pb (11.9%). These data are within the same order of magnitude as previously published by Praveena and Omar, (2017). In addition the concentration of metal found in digested rice was reduced by about half with respect to cooked rice. This fact has a direct influence on the estimation of the human risk. Understanding the effect of the digestion process on the bioavailability of each metal is of great relevance to avoid the overestimation of the human risk, as already indicated by some authors (Omar et al., 2013). These differences could be due to the action of the dietary fiber as a chelating agent to remove metallic elements through two pathways: partial or total inhibition of bioavailability, and inhibition of intestinal reabsorption (Borowska et al., 2020; Kachenpukdee and Oonsivilai, 2019). Similarly, the degree of bioavailability of metallic elements decreases in the following order: Sona Masoori >Jasmine >Hispamar >Sole >Memby >Puntal >Barone >Basmati >Guadiamar >Thaiperla, with values ranging between 58.7 ±6.1% in the case of Sona Masoori variety and 32.6 ±4.6% in Thaiperla variety. A thorough study should be carried out to understand how metallic and metalloid elements interact with dietary fiber matrices using in vivo models. However, to our knowledge, there are no studies in which the absorbed metal values were used to calculate the non-carcinogenic and carcinogenic health risks. 3.2. Adult dietary intake assessment Tables 6 and 7 show the estimated daily intake (EDI) values Table 3 Concentration of metallic elements in the digestible fraction of digested rice. Results are expressed as mean ±standard deviation (mg/kg) (n =3). Rice varieties Al Cr Mn Ni Cu Zn As Se Cd Pb Barone 7.00 ±0.05 e 0.20 ±0.00 ab 21.00 ±0.10 c 0.13 ±0.01 bc 5.30 ±0.01 e 59.00 ±0.50 gh 0.33 ±0.00 c 0.19 ±0.00 cde 0.05 ±0.00 f 0.09 ±0.00 a Basmati 5.50 ±0.10 bc 0.19 ±0.00 a 44.00 ±1.00 e 0.16 ±0.01 d 6.5 ±0.01 f 54.00 ±0.60 f 4.10 ±0.01 f 0.17 ±0.01 d 0.02 ±0.00 c 0.07 ±0.00 a Guadiamar 11.00 ±0.30 f 0.24 ±0.01 cde 17.00 ±0.60 b 0.09 ±0.01 a 5.30 ±0.30 e 54.00 ±0.20 fg 0.09 ±0.00 a 0.16 ±0.00 bcd 0.01 ±0.01 ab 0.19 ±0.01 a Hispamar 4.80 ±0.10 ab 0.02 ±0.01 abc 20.00 ±0.90 c 0.09 ±0.00 a 4.50 ±0.20 cd 27.00 ±0.10 a 0.08 ±0.00 a 0.13 ±0.01 a 0.02 ±0.00 b 0.00 ±0.00 a Jasmine 5.10 ±0.09 abc 0.26 ±0.01 de 11.00 ±0.10 a 0.16 ±0.01 de 3.20 ±0.05 b 41.00 ±0.50 cd 0.10 ±0.00 ab 0.14 ±0.00 ab 0.02 ±0.00 c 0.05 ±0.00 a Marisma 5.30 ±0.09 abc 0.22 ±0.00 bcd 21.00 ±0.20 c 0.09 ±0.00 a 7.10 ±0.01 f 39.00 ±0.31 bc 0.09 ±0.00 ab 0.14 ±0.00 ab 0.01 ±0.00 a 0.04 ±0.00 a Memby 5.10 ±0.04 abc 0.27 ±0.02 ef 31.00 ±0.30 d 0.43 ±0.01 h 5.60 ±0.09 e 51.00 ±0.31 ef 0.52 ±0.00 d 0.16 ±0.00 bc 0.02 ±0.00 b 0.06 ±0.00 a Perlado 6.30 ±0.10 de 0.32 ±0.01 g 17.00 ±0.30 b 0.31 ±0.00 f 3.50 ±0.00 b 40.00 ±0.40 c 0.09 ±0.00 a 0.17 ±0.01 bcd 0.01 ±0.00 a 0.07 ±0.00 a Pi˜ nana 5.70 ±0.10 cd 0.43 ±0.00 h 21.00 ±0.50 c 0.34 ±0.01 fg 3.90 ±0.06 bc 34.00 ±0.30 b 0.10 ±0.00 ab 0.18 ±0.00 cd 0.01 ±0.00 a 0.06 ±0.00 a Puntal 4.80 ±0.10 ab 0.20 ±0.00 abc 18.00 ±0.20 bc 0.08 ±0.00 a 4.90 ±0.05 de 34.00 ±0.10 b 0.10 ±0.00 abcde 0.18 ±0.00 cd 0.01 ±0.00 ab 0.05 ±0.00 a Sendra 4.60 ±0.10 a 0.20 ±0.01 ab 34.00 ±1.00 d 0.10 ±0.00 ab 5.70 ±0.07 e 46.00 ±1.00 de 0.11 ±0.00 ab 0.18 ±0.01 cd 0.01 ±0.00 a 0.05 ±0.00 a Sole 5.00 ±0.08 abc 0.29 ±0.01 fg 19.00 ±0.20 bc 0.36 ±0.01 g 2.20 ±0.00 a 37.00 ±0.50 bc 0.21 ±0.00 bc 0.13 ±0.01 a 0.03 ±0.00 d 0.05 ±0.00 a Sona Masoori 5.10 ±0.30 abc 0.27 ±0.01 ef 9.40 ±0.50 a 0.15 ±0.01 cd 4.40 ±0.30 cd 34.00 ±2.00 b 0.1 ±0.00 ab 0.23 ±0.00 e 0.04 ±0.00 e 0.05 ±0.00 a Thaiperla 5.00 ±0.10 abc 0.22 ±0.00 abc 0.34 ±0.20 d 0.19 ±0.00 e 5.10 ±0.00 de 0.60 ±0.10 h 0.97 ±0.01 e 0.26 ±0.00 f 0.32 ±0.00 d 0.10 ±0.00 a Average 5.74 0.26 22.67 0.19 4.80 43.57 0.23 0.18 0.02 0.06 Standard desviation 1.60 0.06 9.60 0.11 1.30 10.40 0.03 0.04 0.01 0.04 Different letters in the same columns indicate significant differences by ANOVA test (p ≤0.05). Table 4 Mean and SD of metal in relation with type of grain (mg/Kg) (n =3). Al Cr Mn Ni Cu Zn As Se Cd Pb Average of metal in long grain 1.1⋅10 -4 5.6⋅10 -4 390⋅10 -4 5.5⋅10 -4 70⋅10 -4 820⋅10 -4 3.6⋅10 -4 2.9⋅10 -4 0.5⋅10 -4 1.3⋅10 -4 Standard deviation of long grain 17⋅10 -4 1.4⋅10 -4 210⋅10 -4 2.7⋅10 -4 19⋅10 -4 170⋅10 -4 23⋅10 -4 0.7⋅10 -4 0.2⋅10 -4 2.1⋅10 -4 Average of metal in short grain 100⋅10 -4 4.9⋅10 -4 420⋅10 -4 4.5⋅10 -4 73⋅10 -4 790⋅10 -4 3.1⋅10 -4 2.8⋅10 -4 0. 5⋅10 -4 1.2⋅10 -4 Standard deviation of short grain 93⋅10 -4 1.9⋅10 -4 100⋅10 -4 2.4⋅10 -4 43⋅10 -4 420⋅10 -4 2.1⋅10 -4 1.2⋅10 -4 0.4⋅10 -4 2.0⋅10 -4 J.R. Aguilera-Vel´ azquez et al. Journal of Food Composition and Analysis 117 (2023) 105101 6 considering the estimated rice consumption in Spain and the mean content of the corresponding element. In order to compare the values of EDI calculated in the present study, some safe limits for the studied elements were taken into account when available. Also, the percentages of contribution to the reference values for adults were calculated taking into account the mean EDI for all the varieties (Table 8). Thus, daily average consumption of rice does not significantly expose the Spanish consumer to unsafe levels of Cr if the element detected was Cr (III), Ni, Pb, and Cd with 0.29%, 0.000018%, 0.000017%, and 0.04% of dietary contribution, respectively. However, the percentage of contribution of Al is higher (45.40%), especially considering that Al is an element that is found in all foods in variable concentrations. Similar results were found by Rubio-Armend´ ariz et al. (2021) in some types of cereal (barley gofio and corn) which had around a 30% of the contribution of Al to get the TWI. Finally, rice provides a large contribution to the global dietary intake of Cu and Zn (72.63% and 62.25%, respectively). Therefore, it is necessary to regulate the content of these metals in rice by setting maximum concentration levels, mainly in those countries where rice is an important and essential part of the diet. Table 5 Metallic bioavailability values. Rice varieties Bioaccessibility (%) Al Cr Mn Ni Cu Zn As Se Cd Pb Barone 73.2 24.9 41.7 70.2 41.2 25.0 63.6 20.7 20.7 12.3 Sona Masoori 78.4 21.9 93.1 70.8 58.9 42.1 87.8 52.6 69.3 12.4 Perlado 79.4 15.6 54.5 25.5 46.1 25.1 98.9 12.0 39.4 10.4 Jasmine 74.7 16.8 70.2 96.5 52.0 36.7 90.6 18.3 37.6 11.6 Sole 80.8 16.7 63.8 30.8 50.0 34.3 98.2 13.1 95.4 12.0 Memby 79.8 18.3 57.3 87.7 47.1 29.2 59.6 10.8 70.7 10.0 Marisma 71.3 17.4 41.8 78.8 43.7 36.3 93.7 12.9 25.6 12.2 Sendra 82.1 27.0 42.9 91.9 34.7 29.6 92.6 8.3 32.5 17.4 Pi˜ nana 80.7 33.9 37.7 87.2 35.8 26.6 88.0 10.0 29.2 12.2 Hispamar 92.3 28.7 59.1 95.5 42.9 41.9 90.2 11.0 20.9 16.3 Basmati 80.0 28.5 43.2 55.2 36.7 33.5 37.0 12.1 43.3 13.8 Puntal 91.7 21.6 43.7 62.5 44.8 32.0 89.3 11.7 43.5 14.1 Guadiamar 43.8 32.1 40.1 59.2 25.7 19.6 89.5 8.1 16.8 4.3 Thaiperla 82.0 20.9 32.1 39.9 30.8 21.2 39.2 9.6 42.5 8.2 Table 6 EDI values in cooked rice (mg/kg). Cooked rice EDI (mg/kg) Al Cr Mn Ni Cu Zn As Se Cd Pb Barone 0.75 0.01 1.29 0.01 0.32 2.17 0.03 0.006 0.002 0.002 Basmati 0.65 0.01 2.79 0.04 0.35 2.64 0.22 0.003 0.002 0.001 Guadiamar 0.67 0.01 0.98 0.02 0.20 1.55 0.01 0.002 0.000 0.001 Hispamar 0.65 0.01 1.72 0.01 0.28 1.66 0.01 0.002 0.001 0.001 Jasmine 0.56 0.01 1.08 0.02 0.24 2.24 0.02 0.004 0.001 0.001 Marisma 0.56 0.01 1.28 0.02 0.45 2.05 0.01 0.003 0.001 0.001 Memby 0.60 0.01 2.64 0.06 0.39 2.13 0.05 0.003 0.002 0.001 Perlado 0.73 0.01 1.32 0.01 0.23 1.47 0.01 0.003 0.001 0.001 Pi˜ nana 0.67 0.02 1.14 0.04 0.20 1.33 0.01 0.003 0.001 0.001 Puntal 0.65 0.01 1.17 0.02 0.32 1.61 0.01 0.003 0.001 0.001 Sendra 0.82 0.01 2.14 0.01 0.29 1.99 0.01 0.002 0.001 0.001 Sole 0.59 0.01 1.73 0.02 0.16 1.86 0.03 0.002 0.005 0.001 Sona Masoori 0.59 0.01 1.28 0.03 0.38 2.10 0.01 0.018 0.005 0.001 Thaiperla 0.60 0.01 1.60 0.01 0.23 1.86 0.06 0.004 0.002 0.001 Table 7 EDI values in digested rice (mg/kg). Digested rice Al Cr Mn Ni Cu Zn As Se Cd Pb Barone 0.46 0.01 1.39 0.01 0.35 3.91 0.02 0.01 0.004 0.006 Basmati 0.55 0.02 4.37 0.02 0.65 5.33 0.40 0.02 0.002 0.007 Guadiamar 0.44 0.01 0.70 0.00 0.22 2.26 0.00 0.01 0.001 0.008 Hispamar 0.42 0.02 1.74 0.01 0.40 2.37 0.01 0.01 0.001 0.004 Jasmine 0.36 0.02 0.74 0.01 0.23 2.94 0.01 0.01 0.002 0.004 Marisma 0.50 0.02 1.93 0.01 0.66 3.59 0.01 0.01 0.001 0.004 Memby 0.36 0.02 2.21 0.03 0.39 3.48 0.04 0.01 0.001 0.005 Perlado 0.41 0.02 1.06 0.02 0.22 2.57 0.01 0.01 0.001 0.004 Pi˜ nana 0.58 0.04 2.11 0.03 0.40 3.50 0.01 0.02 0.001 0.006 Puntal 0.47 0.02 1.78 0.01 0.48 3.39 0.01 0.02 0.001 0.005 Sendra 0.45 0.02 3.33 0.01 0.56 4.51 0.01 0.02 0.001 0.005 Sole 0.35 0.02 1.31 0.03 0.16 2.63 0.02 0.01 0.002 0.003 Sona Masoori 0.37 0.02 0.69 0.01 0.32 2.50 0.01 0.02 0.003 0.003 Thaiperla 0.55 0.02 3.71 0.02 0.56 6.54 0.11 0.03 0.004 0.011 J.R. Aguilera-Vel´ azquez et al. Journal of Food Composition and Analysis 117 (2023) 105101 7 3.3. Relationship among the elements Mineral elements exist together or separately in nature. The presence of these elements may have different features and their co-existence may intensify the toxicity of the metals and metalloids with their intake, increasing the potential risk. On the other hand, metals with physiological functions could exert a protective role against damage caused by toxic elements, acting as their antagonists (Brizio et al., 2016; Basaran, 2022). Thus, a correlation analysis was carried out in order to find the potential relationships among metallic and metalloid elements in rice from different locations (Spanish and imported). The sample correlation matrix indicates the degree of correlation between the several metalpairs of variables at a glance. However, in order to statistically prove the possible correlation, according to the before-mentioned nonnormality distribution of the elements, the non-parametric technique of Spearman (Miller and Miller, 2000) was applied. The most important correlations were those with a Spearman coefficient close to 1 value. Significant correlations (p≤0.05) between pairs with a Spearman coefficient R close to 1 of the detected elements were observed (Figs. 1a and 1b), this is a consequence of the strong interrelation of these elements. Differences were found between rice from different origins. Strong correlations R >0.5 (p≤0.05) between Cr-Al, Zn-As, Zn-Se, Se- As, and Pb-Se were detected in rice from Spain (Fig. 1a) while the correlations R>0.5 (p ≤0.05) was observed in rice imported and sold in local markets in Pb-Al, Pb-Zn, As-Mn and Ni-Cr (Fig. 1b). These Table 8 Mean EDI for all the varieties in digested and cooked rice and Intake limit values of the toxic metals analyzed. Toxic Metal Mean EDI digested rice Mean EDI cooked rice Parameter Intake Limit Values Organization References Cr(III) 0.02 0.01 TDI 0.3 mg/kgbw/day EFSA EFSA, 2014 Ni 0.02 0.02 13 g/kgbw/day EFSA, 2020 Pb 0.01 0.00 BMDL 0.63 g/kgbw/day 1 1.50 g/kgbw/day 2 EFSA, 2010 Al 0.45 0.65 TWI 1 mg/kgbw/week EFSA EFSA, 2011a Cd 0.002 0.002 2.5 g/kgbw/week EFSA, 2011b Cu 0.40 0.29 SL 14–21 mg/person/week Ellenhorn, 1997 Zn 3.54 1.90 15 mg/person/week Hern´ andez and Sastre, 1999 EFSA, European Food Safety Authority; WHO,World Health Organization; BMDL, benchmark dose level; TDI, Tolerable daily intake; TWI, tolerable weekly intake; bw, body weight; nephrotoxicity 1 and cardiovascular effects 2. Fig. 1. Spearman coeffiecients (R) between several pairs of variables (metals and metalloids) in a) rice from Spain b) imported rice. *p ≤0.0.5, * *p≤0.01. The bold meaning is that the R value are nearest of one value. J.R. Aguilera-Vel´ azquez et al. Journal of Food Composition and Analysis 117 (2023) 105101 8 differences are related to soil composition and the agronomic biofortification programs of the different countries (Kumar et al., 2018; Roda et al., 2020). Regarding correlations in rice from Spain, a significant correlation between Cr and Al was observed. Both of them are present in the Earth’s crust, thus, most rocks and soils can contain appreciable amounts of these two elements at the same time (Ali et al., 2011). Indeed, an increase of Cr levels in the environment has been monitored, due to the increased use with industrial applications, such as electroplating, catalysts production, refractory steel production, and metallurgical applications (Ranieri et al., 2022). Excessive levels of toxic elements in the soil will increase the risk of ingesting toxic metals into the human body through the food chain or soil dust, especially for children (Xu et al., 2022). Thus, human exposure to Cr is mainly derived from soil-crop systems, because Cr is easily integrated into the food chain (Anand et al., 2018; Ao et al., 2022). This fact was demonstrated by Adhikari et al. (2022), who detected an exceeded content not only for Cr but also for Al and other potentially toxic elements in leaves from medicinal plants and vegetables. Also, strong correlations between Zn-Se As-Se, and Pb-As can be observed in Spanish rice. Despite the decrease of Pb levels in the environment in the last decades due to its removal from fuel, there are other many anthropogenic sources of this metal, such as intensive agronomic activities, by the use of pesticides and fertilizers, or mining activities (Chamannejadian et al., 2013; Xu et al., 2022). Furthermore, Zn in soils is mainly associated with mining activities as well. Moreover, trace metal contamination in paddy soil-rice systems through mining activities following the release of untreated wastes into the environment is considered a significant issue worldwide (Gonz´ alez-Corrochano et al., 2014). One of the main rice-growing areas of Spain would be the South of the country, in the province of Seville, near an important mining region in Huelva (minas de Río Tinto), which could explain the presence of this element in the environment. Other elements such as As, or Cr, trace elements comprising pesticides, are present in paddy soils increasing the uptake of these elements by paddy rice (Ali et al., 2011). Also, Se is present naturally in soils but its bioavailability to the crop is pH dependent, so Se levels in rice usually come from its use as fertilizer to improve crop growth (L´ opez-Bellido, 2014). In rice of foreign origin, several correlations can be observed with Pb, which could be explained not only by agricultural or industrial activities as in Spanish rice but also by the presence of Pb in the fuels in those countries where is not suppressed (India or Vietnam). Moreover, the contamination status of paddy soil-rice systems in major rice-growing countries, including India or Vietnam has been proven (Ali et al., 2020), which could explain the rest of significant correlations observed such as As-Mn and Cr-Ni. In addition, the correlation between Pb and Zn could be explained by the application of Zn in paddy soil contaminated with Pb in order to improve biomass yield (Mardomi et al., 2019). 3.4. Human health risk assessment 3.4.1. Non-carcinogenic risk assessment The values of Individual Target Hazard Quotients (THQ) are given in Table 9. The range of THQ for each metal was: Al (0.001–0.36), Mn (0.001–0.005); Cu (0.001–0.003); Zn (0.001 - 0.003); As (0.006–0.013) and Se (0.000–0.001), giving the general order of Al >As >Zn >Cu >Mn >Se >Ni >Cd for target adults in all samples. Similar rank order was observed inAbtahi et al. (2017), giving the highest THQ value for As, and the lowest for Ni. It should be noted that the THQ values have not shown any value beyond 1 in any of the cases, indicating that the effects of metallic elements accumulated by rice ingestion do not involve a significant health risk. Similarly, the sum of each THQ does not pose any health problem to the rice consumer, because none of the values is beyond 1, which would be considered unacceptable in relation to non-carcinogenic health risk. The data obtained in this study are in agreement with those published previously by Guadie et al. (2022), which showed that the values of THQ in Ethiopian rice (red and white) and in imported were within the safe level in all the samples, posing no potential health risks on consuming rice in this country. Also, in a systematic review Abtahi et al. (2017) concluded that since the THQ value was lower than 1, consumers were not exposed to carcinogenic risk by consumption of rice cultivated in Iran. However, Vatanpour et al. (2020) observed high levels of metal accumulation in cultivated rice in Iran, resulting in a THQ value higher than 1, suggesting a considerable health risk for regular rice consumers. Similarly, THQ values higher than 1 were detected for Ni, Cu, As, and Cd in rice and vegetables in Bangladesh, indicating significant health risk for consumers (Proshad et al., 2020). Bielecka et al. (2020), stated that the THQ of rice produced in Asia and consumed by the polish population was 60–120 times higher than rice not produced in Asia. However, in the present study, rice marketed in Spain, even of Asian origin, does not show values dangerous to human health. 3.4.2. Carcinogenic risk assessment The incremental lifetime cancer risk (ILCR) was calculated for inorganic As, Cd, Cr, Ni, and Pb which are either recognized or regarded as probable human carcinogens (Baan, 2012). In the present work, no apparent carcinogenic risk could be associated with all the elements studied, since ILCR values were considered within safe limits (10 -4 ) (Table 10), being the acceptable risk limit established by USEPA (2011) values between 10 -6 to 10 -4 (Rapant et al., 2011). The ILCR values of As, Cd, and Cr were around 10 -5 and 10 -4 , respectively, while Pb and Ni led to ILCR values significantly lower with values around 10 -8 . The ILCR value related to the exposition of As in the present work (about 10 -5 ), poses a small contribution to increase the carcinogenic risk. However, As could be underestimated, since As in rice is present not only as an inorganic component but also as organic derivative (Leone, 1984). In this regard, findings from Proshad et al. (2020) for As contaminated rice Table 9 Target Hazard Quotients. Variety Cr Mn Ni Cu Zn As Se total Barone 0.000 0.002 0.000 0.002 0.003 0.009 0.001 0.017 Basmati 0.000 0.005 0.000 0.002 0.003 0.008 0.001 0.019 Guadiamar 0.001 0.002 0.000 0.002 0.003 0.008 0.000 0.015 Hispamar 0.001 0.002 0.000 0.002 0.001 0.006 0.000 0.012 Jasmine 0.001 0.001 0.000 0.001 0.002 0.007 0.000 0.012 Marisma 0.001 0.002 0.000 0.003 0.002 0.007 0.000 0.014 Memby 0.001 0.003 0.000 0.002 0.002 0.008 0.000 0.017 Perlado 0.001 0.002 0.000 0.001 0.002 0.008 0.000 0.015 Pi˜ nana 0.001 0.002 0.000 0.001 0.002 0.009 0.001 0.016 Puntal 0.001 0.002 0.000 0.002 0.002 0.009 0.001 0.015 Sendra 0.000 0.004 0.000 0.002 0.002 0.009 0.001 0.018 Sole 0.001 0.002 0.000 0.001 0.002 0.006 0.000 0.012 Sona Masoori 0.001 0.001 0.000 0.002 0.002 0.011 0.001 0.017 Thaiperla 0.001 0.004 0.000 0.002 0.003 0.013 0.001 0.022 J.R. Aguilera-Vel´ azquez et al. Journal of Food Composition and Analysis 117 (2023) 105101 9 in Bangladesh or those by Sharafi et al. (2019b) detecting As in three different rice types in Iran showed ILCR values higher than the acceptable limit for cancer risk. Other authors have observed that the cancer risk of Pb was tolerable in China with ILCR values into the range 10 -6 to 10 -4 though the cancer risk levels of As, Cd and Cr was inacceptable (ILCR above 10 -4 ) (Mao et al., 2019). 4. Conclusions The metallic and metalloid levels detected in all the varieties studied are below the toxic threshold. The metallic element with the lowest bioavailability in all the analyzed rice varieties was Pb; while As was the metalloid with the highest bioavailability. Different correlations were observed in rice produced in Spain with those imported, indicating that the presence of metals and metalloids in rice is mainly impacted by soil composition. The non-carcinogenic risk of metals and metalloids in all rice varieties studied is below the toxicity threshold, which is in the range of 1–10% of the toxicity threshold. Regarding the carcinogenic risk, the metal with less contribution to the carcinogenic risk was Pb, in contrast to Cr, which was close to the limit value of 10 -4 . In addition, rice provides a large contribution to global dietary intake of Al, Cu, and Zn (30%, 72.63%, and 62.25%, respectively). Lastly, the results obtained in the present work approach a more realistic metal and metalloid exposure through rice intake, as the cooking and digestion procedures were taken into account, which makes a more realistic risk assessment, performed in most of the cases with measurements obtained in raw material. Thus, performing studies of digestion and bioavailability of these foods in raw, in order to perform a better risk assessment of these pollutants. Fundings This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. CRediT authorship contribution statement Juan Bautista: Conceptualization. Isabel Moreno: Conceptualization. Jos´ e Raúl Aguilera-Vel´ azquez: Methodology, Writing – original draft. Esteban Alonso: Resources. Ana Calleja: Writing – review & editing. Esteban Alonso: Writing – review & editing. Conflicts of interest The authors declare no conflict of interest. Data Availability Data will be made available on request. Acknowledgement Authors would like to thank to Dr. Pilar Carbonero, Dr. María Gracia Hinojosa and to PhD student Irene Martin for assistance throughout the research. Appendix A. Supporting information Supplementary data associated with this article can be found in the online version at doi:10.1016/j.jfca.2022.105101. References Abtahi, M., Fakhri, Y., Oliveri Conti, G., Keramati, H., Zandsalimi, Y., Bahmani, Z., Hosseini Pouya, R., Sarkhosh, M., Moradi, B., Amanidaz, N., Ghasemi, S.M., 2017. Heavy metals (As, Cr, Pb, Cd and Ni) concentrations in rice (Oryza sativa) from Iran and associated risk assessment: a systematic review. 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Variety Ni Cd Cr As Total Barone 1.3•10 -8 5.7•10 -5 1.4•10 -4 8.4•10 -5 2.8•10 -4 Basmati 7.7•10 -9 2.5•10 -5 1.3•10 -4 1.0•10 -4 1.6•10 -4 Guadiamar 1.7•10 -8 1.4•10 -5 1.7•10 -4 2.2•10 -5 2.1•10 -4 Hispamar 9.6•10 -9 1.8•10 -5 1.5•10 -4 2.1•10 -5 1.9•10 4 Jasmine 1.2•10 -8 2.5•10 -5 1.8•10 -4 2.9•10 -5 2.4•10 -4 Marisma 1.0•10 -8 1.3•10 -5 1.6•10 -4 2.3•10 -5 2.0•10 -4 Memby 1.2•10 -8 1.8•10 -5 2.0•10 -4 1.3•10 -4 3.5•10 -4 Perlado 9.7•10 -8 1.1•10 -5 2.3•10 -4 2.3•10 -5 2.6•10 -4 Pi˜ nana 7.2•10 -9 1.1•10 -5 3.1•10 -4 2.5•10 -5 3.5•10 -4 Puntal 7.9•10 -8 1.4•10 -5 1.5•10 -4 2.6•10 -5 1.9•10 -4 Sendra 1.7•10 -9 1.2•10 -5 1.4•10 -4 2.8•10 -5 1.8•10 -4 Sole 1.2•10 -8 3.5•10 -5 2.1•10 -4 5.4•10 -5 3.0•10 -4 Sona Masoori 1.2•10 -8 4.7•10 -5 1.9•10 -4 2.5•10 -5 2.7•10 -4 Thaiperla 6.1•10 -9 3.4•10 -5 1.5•10 -4 2.5•10 -5 4.4•10 -4 J.R. Aguilera-Vel´ azquez et al.