An optimized method for simultaneous determination of cadmium, arsenic, nickel and copper bioaccessibility in rice

Abstract

The application discloses an optimized method for synchronously determining the bioaccessibility of cadmium, arsenic, nickel and copper in rice. The application takes rice as a research object, and discusses the influence of factors such as liquid-to-material ratio, pH of digestive juice, digestion time and cholate concentration on the bioaccessibility of cadmium, arsenic, nickel and copper in rice. Based on the single-factor experiment results, the significant factors influencing the bioaccessibility of cadmium, arsenic, nickel and copper in rice are further optimized by using a Plackett-Burman design and a Box-Behnken response surface method, an in-vitro digestion prediction model is constructed, and the optimal digestion condition is determined by combining an expectation function method. The recovery rate of the model is between 95.72% and 104.45%, the intra-day and inter-day precision is between 0.99% and 4.63%, the method is accurate and reliable, and has good reproducibility; meanwhile, the oral digestion link is reduced, the gastrointestinal digestion stage time is shortened, the experimental cost can be reduced as a whole, the experimental period can be shortened, and the detection efficiency can be improved.

Description

Technical Field

[0001] This invention belongs to the technical field of heavy metal detection, specifically involving an optimized method for simultaneously determining the bioavailability of cadmium, arsenic, nickel, and copper in rice. Background Technology

[0002] With rapid industrialization and urbanization, mining and smelting, urban sewage discharge, and the extensive use of pesticides and fertilizers containing heavy metals have led to a continuous increase in the accumulation of heavy metals in farmland soil systems. According to the "National Soil Pollution Status Survey Bulletin" released by the Ministry of Environmental Protection and the Ministry of Land and Resources, the environmental quality of farmland soil in my country is worrying, with a pollutant exceedance rate of 19.4% at arable land soil sampling points. The main pollutants are heavy metals or metalloids such as cadmium, nickel, arsenic, copper, and mercury. Rice is one of my country's important food crops and a major cereal crop with a strong absorption capacity for heavy metals. Incidents of heavy metal contamination in rice due to farmland soil pollution occur frequently. Long-term consumption of rice with excessive heavy metals poses a risk of chronic poisoning. Therefore, the scientific evaluation of the health risks of heavy metals in rice to humans is receiving increasing attention.

[0003] Domestic and international studies have already assessed the health risks of heavy metals in rice based on the total amount of heavy metals in the rice. For example, Cui et al. (2022) collected rice samples from 14 prefecture-level cities in Hunan Province and assessed the health risks of heavy metals in rice. The results showed that the exceedance rates of arsenic, nickel, and cadmium in rice were 100%, 47.05%, and 44.12%, respectively, indicating potential non-carcinogenic and carcinogenic risks. Cao et al. (2023) assessed the health risks of heavy metals in rice from my country's three major rice-producing areas. The results showed that heavy metals in rice pose potential non-carcinogenic and carcinogenic risks to humans, with arsenic, nickel, and cadmium being the main contributors. However, the above studies usually assume that heavy metals in food can be completely dissociated and fully utilized by the human body in the gastrointestinal environment. In reality, the digestion and absorption of heavy metals in the human gastrointestinal tract is a complex process involving the secretion of digestive juices, enzyme reactions, and gastrointestinal motility. The form, physicochemical properties, and binding form of heavy metals with the food matrix all affect the proportion of heavy metals transported through the gastrointestinal wall into the bloodstream. Therefore, assessing the health risks of heavy metals in rice based on the total amount may overestimate their toxic effects and health risks.

[0004] Bioaccessibility primarily refers to the ratio of the amount of heavy metals released from the food matrix into the gastrointestinal fluid during gastrointestinal digestion to the total amount ingested. Research on heavy metal bioaccessibility allows for a more accurate determination of the amount of heavy metals that the human body can absorb, providing a more precise indicator for health risk assessment. Currently used in vitro simulated digestion models include the physiologically based extraction test (PBET), the in-vitro gastrointestinal method (IVG), the standardized static in vitro digestion method (INFOGEST), and the unified barge method (UBM). Existing technologies suffer from the following core technical deficiencies: 1. The composition of digestion fluid, pH, digestion time, enzyme concentration and other parameters of different models vary greatly. The detection results of heavy metal bioavailability in the same sample can vary by more than 30%. At present, there is no unified and standardized detection method for rice matrix.

[0005] 2. Existing models often include an oral digestion stage. For cooked grain matrices like rice, the oral digestion stage contributes less than 1% to the leaching of heavy metals, but it increases the number of operational steps and systematic errors. Some models have complex digestion fluid compositions (such as UBM and INFogEST models, which contain more than 20 components), resulting in high reagent costs, cumbersome operation, and easy introduction of matrix interference, which affects the stability of the detection results.

[0006] 3. Existing methods cannot balance detection efficiency and result accuracy. Shortening the digestion time will lead to insufficient dissolution of heavy metals, while extending the digestion time will lead to a significant increase in the experimental cycle. In addition, the bioavailability detection results of some elements are low due to re-adsorption.

[0007] Therefore, it is necessary to construct a suitable detection method for the bioavailability of cadmium, arsenic, nickel, and copper in rice by comparing different model methods and optimizing model parameters. This method should achieve the highest detection efficiency for cadmium, arsenic, nickel, and copper, thereby more objectively and scientifically assessing the health risks of cadmium, arsenic, nickel, and copper in rice to the human body.

[0008] In the field of food safety risk assessment and food contaminant detection, although arsenic belongs to metalloids, its toxic effects, environmental behavior, and detection methods are highly consistent with those of heavy metals. Therefore, my country's national food safety standards include arsenic, along with heavy metals such as cadmium, mercury, lead, nickel, and copper, in the scope of control of harmful elements in food. The "heavy metals" mentioned in this invention all include arsenic. Summary of the Invention

[0009] To overcome the shortcomings of the prior art, the purpose of this invention is to provide an optimized method for the simultaneous determination of the bioavailability of cadmium, arsenic, nickel, and copper in rice, so as to achieve the maximum detection efficiency of cadmium, arsenic, nickel, and copper bioavailability.

[0010] To achieve the above objectives, the present invention adopts the following technical solution: An optimized method for simultaneous determination of the bioavailability of cadmium, arsenic, nickel, and copper in rice includes the following steps: S1. Randomly collect rice contaminated with heavy metals, and after processing and cooking, determine the total amount of cadmium, arsenic, nickel and copper in the rice. S2. Compare the effects of different in vitro digestion models on the bioavailability of cadmium, arsenic, nickel and copper in rice, and screen out the optimal in vitro digestion model. S3. Based on the optimal in vitro digestion model, the effects of liquid-to-solid ratio, gastric juice pH, gastric digestion time, intestinal juice pH, intestinal digestion time, and bile salt concentration on the bioavailability of cadmium, arsenic, nickel, and copper were determined by single-factor experiments. S4. Based on the results of the single-factor experiment, design a Plackett-Burman screening experiment to determine the significant factors, and conduct a Box-Behnken response surface experiment based on the significant factors. S5. Fit the response surface experimental results data to construct an in vitro digestion prediction model, and use the expected value function method to perform multi-objective global optimization with the goal of maximizing the bioavailability of four heavy metals simultaneously, and determine the optimal digestion conditions.

[0011] In step S1, the processing and cooking conditions are as follows: milling precision 10%, washing twice, rice-to-water ratio 1:1.3, soaking for 0.5 h, and cooking at normal pressure.

[0012] In step S2, the different in vitro digestion models include PBET, IVG, UBM, and INFOGEST.

[0013] In step S4, a three-factor, three-level response surface methodology is performed using gastric juice pH, gastric digestion time, and intestinal digestion time. The gastric juice pH is 1.0, 1.5, and 2.0; the gastric digestion time is 1.0, 1.5, and 2.0 h; and the intestinal digestion time is 2.0, 3.0, and 4.0 h.

[0014] The construction process of the prediction model also includes: S5, performing interaction factor effect analysis, optimization verification, expected value analysis, and result quality control on the cadmium, arsenic, nickel, and copper bioavailability model equation.

[0015] The optimal digestion conditions are: liquid-to-solid ratio 150:1, gastric juice pH 1.6, gastric digestion time 1.5 h, intestinal juice pH 6.5, intestinal digestion time 3.0 h, and bile salt concentration 2.5 g / L.

[0016] An in vitro digestion method for simultaneously determining the bioavailability of cadmium, arsenic, nickel, and copper in rice, based on the optimized method described above, includes the following steps: (a) The rice samples were milled, washed, cooked and freeze-dried to obtain standardized samples; (b) The sample was mixed with simulated gastric fluid at a liquid-to-solid ratio of 150:1 and digested with shaking at 37°C for 1.5 hours under pH 1.6 conditions; (c) Add simulated intestinal fluid to the digestion solution obtained in step (b), adjust the pH to 6.5, and the bile salt concentration to 2.5 g / L. Digest at 37°C with shaking for 3.0 hours. (d) After digestion, centrifuge and filter, and determine the concentrations of cadmium, arsenic, nickel and copper in the digestate; (e) Calculate the bioavailability of the four heavy metals based on the ratio of heavy metal content in the digestive fluid to the total content in the rice sample; The method does not include an oral digestion stage.

[0017] The pretreatment of the rice sample included: milling with a precision of 10%, washing twice, rice-to-water ratio of 1:1.3, soaking for 0.5 hours, and then steaming at normal pressure.

[0018] The simulated gastric juice contains 1.25 g / L pepsin, 0.5 g / L sodium malate, 0.5 g / L sodium citrate, 420 mL / L lactic acid, and 500 μL / L acetic acid; the simulated intestinal juice contains 2.5 g / L bile salts and 0.5 g / L pancreatic enzymes, but does not contain lipase or bovine serum albumin.

[0019] The method described is applied in assessing the health risks of heavy metals in rice or in establishing food safety limits.

[0020] The beneficial effects of this invention are as follows: This invention utilizes a physiological principle extraction method, taking rice as the research object, to explore the effects of factors such as liquid-to-solid ratio, digestive fluid pH, digestion time, and bile salt concentration on the bioavailability of cadmium, arsenic, nickel, and copper in rice. The results showed that the optimal parameters for the bioavailability of these four heavy metals differed. Based on the single-factor experimental results, Plackett-Burman design and Box-Behnken response surface methodology were used to further optimize the significant factors affecting the bioavailability of cadmium, arsenic, nickel, and copper in rice. An in vitro digestion prediction model was constructed, and through multi-objective expected value optimization, the optimal digestion conditions for simultaneously improving the bioavailability of cadmium, arsenic, nickel, and copper in rice were determined. The results showed that a liquid-to-solid ratio of 150:1, gastric juice pH of 1.6, gastric digestion time of 1.5 h, intestinal juice pH of 6.5, intestinal digestion time of 3.0 h, and bile salt concentration of 2.5 g / L could simultaneously improve the detection efficiency of cadmium, arsenic, nickel, and copper in rice under a single-parameter system. The model recovery rate ranged from 95.72% to 104.45%, and the intra-day and inter-day precision ranged from 0.99% to 4.63%. The method is accurate, reliable, and reproducible.

[0021] This invention, by optimizing the parameters of an in vitro digestion model, enables a faster and more accurate simultaneous determination of the bioavailability of cadmium, arsenic, nickel, and copper in rice under a single-parameter system. Compared with the unoptimized PBET model, the bioavailability of cadmium, arsenic, nickel, and copper in rice is improved by 7.17%, 10.31%, 1.56%, and 14.81%, respectively. Simultaneously, it reduces the oral digestion stage and shortens the gastrointestinal digestion time, thereby reducing overall experimental costs, shortening the experimental cycle, and improving detection efficiency. While saving time and costs, it provides a new approach for more objectively and scientifically assessing the health risks of heavy metals cadmium, arsenic, nickel, and copper in rice to humans, and provides a scientific basis for formulating scientific and reasonable heavy metal limit standards. Attached Figure Description

[0022] Figure 1 This study investigated the effects of four in vitro digestion models on the bioavailability of cadmium, arsenic, nickel, and copper in rice, including Rice-A, Rice-B, and Rice-C. Rice-C.

[0023] Figure 2 The effects of liquid-to-solid ratio, gastric juice pH, gastric digestion time, intestinal juice pH, intestinal digestion time, and bile salt concentration on the bioavailability of cadmium, arsenic, nickel, and copper in rice were investigated.

[0024] Figure 3 The response surface and contour plot are the results of the effect of Cd bioavailability, where: a and d are the response surface and contour plots of the effect of the interaction between gastric digestion time and gastric juice pH on Cd bioavailability; b and e are the response surface and contour plots of the effect of the interaction between intestinal digestion time and gastric pH on Cd bioavailability; c and f are the response surface and contour plots of the effect of the interaction between gastric digestion time and intestinal digestion time on Cd bioavailability.

[0025] Figure 4 The response surface and contour plot are the results of the influence of As bioavailability, where: a and d are the response surface and contour plots of the effect of the interaction between gastric digestion time and gastric juice pH on the bioavailability of As. b and e are the response surface and contour plots of the effect of the interaction between intestinal digestion time and gastric pH on the bioavailability of As. c and f are the response surface and contour plots of the effect of the interaction between gastric digestion time and intestinal digestion time on the bioavailability of As.

[0026] Figure 5 The response surface and contour plot are shown for the effect of Ni bioavailability, where: a and d are the response surface and contour plots of the effect of the interaction between gastric digestion time and gastric juice pH on Ni bioavailability; b and e are the response surface and contour plots of the effect of the interaction between intestinal digestion time and gastric pH on Ni bioavailability; c and f are the response surface and contour plots of the effect of the interaction between gastric digestion time and intestinal digestion time on Ni bioavailability.

[0027] Figure 6 The response surface and contour plot are shown for the effect of Cu bioavailability, where: a and d are the response surface and contour plots of the effect of the interaction between gastric digestion time and gastric juice pH on Cu bioavailability; b and e are the response surface and contour plots of the effect of the interaction between intestinal digestion time and gastric pH on Cu bioavailability; c and f are the response surface and contour plots of the effect of the interaction between gastric digestion time and intestinal digestion time on Cu bioavailability.

[0028] Figure 7 It is the expected value of the bioavailability of cadmium, arsenic, nickel, and copper under optimized conditions. Detailed Implementation

[0029] The method of the present invention is described below through specific embodiments. Unless otherwise specified, the technical means used in the present invention are all methods known to those skilled in the art. Furthermore, the embodiments should be understood as illustrative, not limiting the scope of the invention; the essence and scope of the invention are defined only by the claims. For those skilled in the art, various changes or modifications to the material composition and dosage in these embodiments without departing from the essence and scope of the invention also fall within the protection scope of the present invention.

[0030] 1 Experimental Methods (1) In this invention, heavy metal contaminated rice (Rice-A, Rice-B, Rice-C) was randomly collected, dehulled, and milled. The milling precision (the percentage of milling loss to brown rice mass) was 10%. 30 g of milled rice was weighed and placed in an aluminum box, washed twice with an appropriate amount of distilled water, and distilled water was added at a ratio of 1:1.3 between rice and water. The rice was soaked for 0.5 h at room temperature and then steamed in a steamer. The cooked samples were placed in a freeze dryer (Biosafer-10A) and freeze-dried under vacuum at -60℃. The freeze-dried rice powder was stored in a refrigerator at 4℃ for later use.

[0031] (2) Screening of in vitro digestion models By comparing the effects of four in vitro digestion models—PBET, IVG, UBM, and INFUGEST—on the bioavailability of cadmium, arsenic, nickel, and copper in rice, the most suitable in vitro digestion model for studying the bioavailability of cadmium, arsenic, nickel, and copper in rice was selected. The specific digestion steps are as follows: 0.3 g of freeze-dried rice flour was weighed into a 50 mL centrifuge tube, and simulated oral digestive fluid was added. The tube was then placed in a 37℃ constant temperature water bath and shaken at 150 rpm to simulate oral digestion. After extraction, simulated gastric juice was added to the oral stage extract to adjust the pH. The tube was then placed in a 37℃ constant temperature water bath and shaken at 150 rpm to simulate gastric digestion. After extraction, simulated intestinal juice was added to the gastric stage extract to adjust the pH. The tube was then placed in a 37℃ constant temperature water bath and shaken at 150 rpm to simulate intestinal digestion. After extraction, the extract was centrifuged at 4℃ and 5000 r / min for 10 min. The supernatant was collected and filtered through a 0.45 μM filter membrane. The filtrate and chyme were stored at 4℃ for later analysis. Each sample was tested in triplicate. The composition and digestive parameters of the digestive fluids for the four in vitro digestion models are shown in Table 1.

[0032]

[0033] Total heavy metal content was determined using inductively coupled plasma mass spectrometry (ICP-MS, X Series 2, Thermo). 0.25 g of rice flour was weighed, 8 mL of nitric acid was added, and the mixture was placed in a graphite furnace digester (Digi-Block ED54, LabTech) and digested at 120 °C until the volume was less than 1 mL. After cooling, 3 mL of hydrogen peroxide was added, and digestion continued until the volume was less than 1 mL. The volume was then adjusted to 25 mL with 18.2 MΩ deionized water. The digest was evaporated to near dryness in the graphite furnace digester, and the heavy metal content was determined following the same digestion procedure. Quality control samples (Hunan rice component analysis standard material GBW10010) and reagent blanks were prepared simultaneously. Each sample was tested in triplicate.

[0034] (3) Single-factor experiment Based on the selected models, the effects of key factors such as liquid-to-solid ratio (50:1, 100:1, 150:1, 200:1, 250:1), gastric digestion pH (1.0, 1.5, 2.0, 2.5, 3.0), gastric digestion time (0.5, 1.0, 1.5, 2.0, 2.5 h), intestinal digestion pH (6.0, 6.5, 7.0, 7.5, 8.0), intestinal digestion time (1.0, 2.0, 3.0, 4.0, 5.0 h), and bile salt concentration (1.0, 1.75, 2.5, 5.0, 15 g / L) on the bioavailability of cadmium, arsenic, nickel, and copper in rice were studied.

[0035] (4) Plackett-Burman experimental design Based on single-factor experiments, a Plackett-Burman design was used to screen for significant factors affecting the bioavailability of cadmium, arsenic, nickel, and copper in rice. Each factor was tested at both high and low levels, resulting in a total of 12 experimental groups. The experimental factors and levels are shown in Table 2.

[0036]

[0037] (5) Box-Behnken experimental design Based on the Plackett-Burman experiment, a three-factor, three-level Box-Behenken design was used to further optimize the significant factors affecting the bioavailability of cadmium, arsenic, nickel, and copper in rice. The experimental data were subjected to multiple linear regression and binomial fitting to construct an in vitro digestion model and determine the optimal digestion conditions.

[0038] (6) Methodological investigation Three different types of rice were digested in vitro using optimized in vitro digestion model conditions. After centrifugation, the heavy metal content in the digestive fluid and chyme was determined by ICP-MS, and the ratio of the heavy metal content to the total heavy metal content in the rice was calculated to examine the recovery rate of the in vitro digestion model. The same sample was digested in vitro at six different time points within one day, and monitoring was conducted continuously for six days. Intra-day precision and inter-day precision were calculated to examine the stability of the in vitro digestion model.

[0039] 2. Model Optimization Results and Analysis (1) Screening of in vitro digestion models like Figure 1 As shown, the four in vitro digestion models had varying degrees of impact on the bioavailability of Cd, As, Ni, and Cu in rice. The bioavailability of Cd, from highest to lowest, was: PBET > IVG > UBM > INFOGEST, with PBET and IVG models showing significantly higher bioavailability than UBM and INFOGEST. P<0.05). The bioavailability of As, from highest to lowest, is: PBET > UBM > IVG > INFOOGS. Among them, there is no significant difference among the three models PBET, UBM, and IVG, but it is significantly higher than that of INFOOGS. P <0.05). The bioavailability of Ni and Cu, from highest to lowest, is: PBET > INFOGEST > IVG > UBM, with PBET being significantly higher than INFOGEST, IVG, and UBM. P <0.05). In summary, the PBET model has a better detection effect on the bioavailability of Cd, As, Ni and Cu in rice. Therefore, PBET was selected as the in vitro digestion model for studying the bioavailability of Cd, As, Ni and Cu in rice.

[0040] (2) Optimization of single-factor experimental parameters like Figure 2 As shown, within the liquid-to-solid ratio range of 50:1 to 250:1, the bioavailability of Cd initially increased and then decreased with increasing liquid-to-solid ratio, reaching a peak at 150:1, and then gradually declined. The bioavailability of Cu increased significantly within the liquid-to-solid ratio range of 50:1 to 150:1, and then gradually stabilized. The bioavailability of Ni and As showed no significant difference within the liquid-to-solid ratio range of 50:1 to 150:1, and then decreased significantly. P <0.05). This is mainly because the greater the amount of solvent used, the more heavy metals dissolve. When the amount of solvent increases to a certain extent, the solvent's solubility for heavy metals may reach saturation. In summary, Plackett-Burman experimental designs were selected with liquid-to-solid ratios of 100:1 and 200:1.

[0041] Within the gastric juice pH range of 1.0–3.0, the bioavailability of Cd, Ni, and Cu in rice initially increased and then decreased with increasing pH, peaking at pH 1.5 before significantly declining. As bioavailability was highest within the pH range of 1.0–2.0, then significantly decreased with further pH increases. This is mainly because the acidic environment of the gastric phase favors the dissolution of heavy metals; the higher the gastric juice pH, the lower the dissolution rate. Therefore, Plackett-Burman experimental designs were conducted using gastric juice pH values ​​of 1.0 and 2.0.

[0042] Within the gastric digestion time range of 0.5–2.5 h, Cd bioavailability showed no significant difference from 0.5 to 2.0 h, but decreased significantly thereafter. P <0.05); Ni bioavailability was similar to Cd, showing no significant difference in the first 0.5–1.5 h, but then decreased significantly ( P<0.05); the bioavailability of both Cu and As showed a trend of first increasing and then decreasing with the prolongation of gastric digestion time, reaching a peak at 1.5 h, and then decreasing significantly thereafter. P <0.05). This is mainly because prolonged gastric digestion time can increase the leaching of heavy metals. In summary, Plackett-Burman experimental designs were conducted using gastric digestion times of 1 h and 2 h.

[0043] Within the intestinal fluid pH range of 6.0–6.5, the bioavailability of Cd, Ni, and Cu in rice was highest. As the intestinal fluid pH continued to increase, the bioavailability of Cd, Ni, and Cu decreased significantly. P <0.05), and the bioavailability of As did not differ significantly at different pH values. This is mainly because the weakly alkaline environment of intestinal fluid reduces the solubility of heavy metals dissolved in the gastric phase due to precipitation. In conclusion, Plackett-Burman experimental designs were conducted using intestinal fluid pH values ​​of 6.0 and 7.0.

[0044] Within the intestinal digestion time range of 1.0–5.0 h, the bioavailability of the four heavy metals Cd, As, Ni, and Cu all showed a trend of first increasing and then decreasing with the extension of intestinal digestion time, reaching a peak at 3.0 h, and then decreasing significantly thereafter. P <0.05). This is mainly because prolonged intestinal digestion time can increase the leaching of heavy metals. In summary, Plackett-Burman experimental designs were conducted using intestinal digestion times of 2 h and 4 h.

[0045] Within the bile salt concentration range of 1.0–15 g / L, the bioavailability of Cd and Ni gradually increased with increasing bile salt concentration, reaching peak values ​​at 1.75 g / L and 2.5 g / L, respectively, before significantly decreasing. The bioavailability of As gradually increased with increasing bile salt concentration, stabilizing at 2.5 g / L. The bioavailability of Cu showed no significant change among different bile salt concentrations. This is mainly because bile salts can accelerate fat breakdown, releasing heavy metals bound to lipoproteins. Therefore, Plackett-Burman assays were designed with bile salt concentrations of 1.0 g / L and 2.5 g / L.

[0046] (3) Optimization of Plackett-Burman experimental parameters Based on the single-factor experiments, each factor was tested at two levels: liquid-to-solid ratio (A, 100:1 and 200:1), gastric juice pH (B, 1.0 and 2.0), gastric digestion time (C, 1 h and 2 h), intestinal juice pH (D, 6.0 and 7.0), intestinal digestion time (E, 2 h and 4 h), and bile salt concentration (F, 1.0 g / L and 2.5 g / L). Twelve experiments were automatically generated using a Plackett-Burman design, with each experiment repeated at least three times. The experimental design and results are shown in Table 2. Table 3 shows the variance results, indicating that gastric juice pH and intestinal digestion time significantly affected Cd bioavailability; gastric juice pH and gastric digestion time significantly affected As and Cu bioavailability; and gastric juice pH significantly affected Ni bioavailability. P <0.05). In summary, gastric juice pH, gastric digestion time, and intestinal digestion time were selected as factors for optimization in the next step of the response surface methodology. The liquid-to-solid ratio, intestinal juice pH, and bile salt concentration had no significant effects and had a weak impact on bioavailability. Based on the results of the single-factor experiments, the liquid-to-solid ratio of 150:1, intestinal juice pH of 6.5, and bile salt concentration of 2.5 g / L were set for the next step of the study.

[0047]

[0048] (4) Optimization of experimental parameters using response surface methodology 4.1 Response Surface Analysis and Optimization Based on the Plackett-Burman experiment results, with bioavailability as the evaluation index, three factors were selected as independent variables: gastric pH (A), gastric digestion time (B), and intestinal digestion time (C). According to the Box-Benhnken central composite design principle, a three-factor, three-level response surface methodology was used for analysis. The experimental design and results are shown in Table 4.

[0049]

[0050] Multiple regression analysis was performed on the experimental results in the table using Design-Expert software, yielding a quadratic multinomial regression model with Cd bioavailability as the response value: Y = 30.6357 + 33.214 × A + 31.1554 × B + 0.0315282 × C + 0.537834 × AB + 1.12203 × AC - 0.472923 × BC - 11.7637 × A 2 -9.98331×B 2 -0.3074×C 2 An analysis of variance was performed on the model. Table 5 shows the regression equation... P A value <0.0001 indicates that the model has reached a highly significant level and has good predictive ability. The model's correlation coefficient...R 2 =0.9751, Corrected coefficient of determination R 2 Adj =0.9431, indicating that the model can explain 94.31% of the variation in the response value. (Misfit term) P The value = 0.2564 > 0.05, which is not significant, indicating that the model fits well and can analyze and predict Cd bioavailability. The model consists of the first-order terms A and C, and the second-order term A. 2 B 2 Extremely significant ( P <0.01), all interaction terms are not significant. According to P It is worth noting that the order of influence of various factors on Cd bioavailability is: gastric pH > intestinal digestion time > gastric digestion time.

[0051]

[0052] The quadratic multinomial regression model with the biological availability of As as the response value is: Y = 29.85 + 29.2825 × A + 20.5075 × B + 11.635 × C + 1.3 × AB - 0.675 × AC + 0.025 × BC - 9.09 × A 2 -7.49×B 2 -1.735×C 2 An analysis of variance was performed on the model. Table 5 shows the regression equation... P A value <0.0001 indicates that the model has reached a highly significant level and has good predictive ability. The model's correlation coefficient... R 2 =0.9747, Corrected coefficient of determination R 2 Adj =0.9422, indicating that the model can explain 94.22% of the variation in the response value. (Misfit term) P The value = 0.2749 > 0.05, which is not significant, indicating that the model fits well and can analyze and predict the availability of As. The model's linear term A and quadratic term A' are also relevant. 2 B 2 and C 2 Extremely significant ( P <0.01), all interaction terms are not significant. According to P It is worth noting that the order of influence of various factors on the bioavailability of As is: gastric juice pH > intestinal digestion time > gastric digestion time.

[0053] The quadratic multinomial regression model with Ni biological availability as the response value is: Y = 21.1148 + 28.2993 × A + 32.9659 × B + 11.62 × C - 4.53333 × AB + 1.91111 × AC - 0.103704 × BC - 9.25037 × A 2 +-9.04296×B 2 -2.45333×C 2 An analysis of variance was performed on the model. Table 5 shows the regression equation... P A value of 0.0012 indicates that the model is highly significant and has good predictive ability. The model's correlation coefficient... R 2 =0.9455, Corrected coefficient of determination R 2 Adj =0.8755, indicating that the model can explain 87.55% of the variation in the response value. (Misfit term) P The value = 0.8064 > 0.05, which is not significant, indicating that the model fits well and can analyze and predict Ni bioavailability. The model's interaction terms AB and BC, and the quadratic term A... 2 B 2 and C 2 Extremely significant ( P <0.01), the linear term is not significant. According to... P It is worth noting that the order of influence of various factors on Ni bioavailability is: gastric digestion time > intestinal digestion time > gastric pH.

[0054] The quadratic multinomial regression model with Cu bioavailability as the response value is: Y = 67.3116 + 12.5433 × A + 16.6839 × B + 0.323187 × C + 2 × AB + 1.225 × AC + 1.62125 × BC - 5.68025 × A 2 -8.42775×B 2 -0.794437×C 2 An analysis of variance was performed on the model. Table 5 shows the regression equation... P A value of 0.0002 indicates that the model is highly significant and has good predictive ability. The model's correlation coefficient... R 2 =0.9684, Corrected coefficient of determination R 2 Adj =0.9278, indicating that the model can explain 92.78% of the variation in the response value. (Misfit term) P The value = 0.3009 > 0.05, which is not significant, indicating that the model fits well and can analyze and predict Cu bioavailability. The model consists of the linear term A, interaction terms AC and BC, and the quadratic term A.2 B 2 and C 2 Significant ( P <0.05). According to P It is worth noting that the order of influence of various factors on Cu bioavailability is: gastric pH > gastric digestion time > intestinal digestion time.

[0055] 4.2 Interaction Analysis of Various Factors Using Origin software, response surface plots and contour plots were generated based on the model to illustrate the effects of the interactions of A, B, and C on Cd bioavailability. Figure 3 It can be seen that the 3D response curves of the interaction between gastric juice pH and intestinal digestion time, and the contour plots of gastric digestion time and intestinal digestion time are relatively steep, and the contour plots tend to be elliptical, indicating that the interaction between the two has a strong impact on Cd bioavailability. The 3D surface plots of gastric juice pH and gastric digestion time are steep, but the contour plots tend to be circular, indicating that the interaction between the two is weak, consistent with the results of the analysis of variance. Figure 4 The 3D response curves for the interactions between gastric juice pH and gastric digestion time, gastric juice pH and intestinal digestion time, and gastric digestion time and intestinal digestion time are relatively gentle, and the contour plots are close to circular, indicating that the interaction among the three factors has a weak impact on the bioavailability of As, consistent with the results of the analysis of variance. Figure 5 It can be seen that the 3D response curves of the interactions between gastric juice pH and gastric digestion time, and between gastric juice pH and intestinal digestion time, are relatively steep, and the contour plots tend to be elliptical, indicating that the interaction between the two has a strong impact on Ni bioavailability. The 3D surface plots of gastric digestion time and intestinal digestion time are relatively flat, and the contour plots tend to be circular, indicating that the interaction between the two is weak, consistent with the results of the analysis of variance. Figure 6 It can be seen that the 3D response curves of the interaction between gastric juice pH and intestinal digestion time, and the contour plots of gastric digestion time and intestinal digestion time are relatively steep, and the contour plots tend to be elliptical, indicating that the interaction between the two has a strong impact on Cu bioavailability. The 3D surface plots of gastric juice pH and gastric digestion time are relatively flat, and the contour plots tend to be circular, indicating that the interaction between the two is weak, consistent with the results of the analysis of variance. Figure 3 , Figure 4 The response surface slope indicates that the degree of influence on Cd and As is A>C>B; From Figure 5 It can be seen that the degree of influence of each factor on Ni is B>C>A; from Figure 6 It can be seen that the degree of influence on Cu is A>B>C.

[0056] 4.3 Model Optimization Based on the model fitting results, the predicted optimal digestion conditions under ideal conditions are: gastric juice pH 1.57901, gastric digestion time 1.47797 h, and intestinal digestion time 2.92932 h. Considering the feasibility of actual operation, the theoretical values ​​obtained from the regression equation are revised to: gastric juice pH 1.6, gastric digestion time 1.5 h, and intestinal digestion time 3.0 h.

[0057] 4.4 Expected Value Test To address the technical bottleneck of single-factor optimization failing to simultaneously optimize four elements, this invention employs the Desirability Function method. With the simultaneous maximization of the bioavailability of cadmium, arsenic, nickel, and copper as the optimization objective, a multi-objective global optimization is performed on the constructed response surface model. In the Desirability Function method, the value ranges from 0 to 1, where 0 represents a completely unacceptable result and 1 represents the theoretical optimal value of the indicator. The closer the overall expected value is to 1, the better the effect of multi-objective simultaneous optimization. The results are as follows... Figure 7 As shown, the expected values ​​of the three core optimization variables—gastric pH (A), gastric digestion time (B), and intestinal digestion time (C)—are all 1, indicating that the final determined optimal parameter combination falls entirely within the ideal value range of the three variables, and the parameter values ​​themselves have reached the theoretical optimal state. The expected values ​​of the bioavailability of the four heavy metals range from 0.854 to 0.956, all close to the optimal value of 1, indicating that the bioavailability of the four elements under the optimization conditions is close to the theoretical maximum value. The comprehensive expected value reaches 0.921, close to the optimal value of 1, and is the only globally optimal solution output by the model.

[0058] 4.4 Model Validation The model was validated based on the results of response surface methodology experiments and actual conditions. Under optimal digestion conditions (gastric juice pH 1.6, gastric digestion time 1.5 h, intestinal juice pH 6.5, intestinal digestion time 3.0 h, liquid-to-solid ratio 150:1, and bile salt concentration 2.5 g / L), the measured values ​​of bioavailability of Cd, As, Ni, and Cu in rice were 80.47%, 84.26%, 83.27%, and 90.24%, respectively. The errors between these values ​​and the model predictions (79.84%, 86.59%, 82.50%, and 90.69%) were 0.50%–2.76%, indicating that the established regression equation model can accurately reflect the influence of each factor on bioavailability.

[0059] (5) Results quality control 5.1 Recovery rate As shown in Table 6, under the optimized model conditions, the recoveries of Cd, As, Ni, and Cu in different types of rice were 96.37%–103.80%, 102.51%–104.45%, 101.75%–103.46%, and 95.72%–103.42%, respectively, all within the range of 95%–105%. This demonstrates that the model has good accuracy and can be applied to the study of the bioavailability of Cd, As, Ni, and Cu in different types of rice.

[0060]

[0061] 5.2 Intra-day and inter-day precision As shown in Tables 7 and 8, under the optimized model conditions, the intra-day precision of Cd, As, Ni, and Cu in different rice varieties was 1.70%–3.39%, 0.99%–3.43%, 1.11%–2.38%, and 2.10%–3.44%, respectively, and the inter-day precision was 1.62%–2.84%, 1.49%–3.99%, 1.29%–2.56%, and 1.19%–4.63%, respectively, all less than 5%. This demonstrates that the model has good stability and high reproducibility, and can be applied to the study of the bioavailability of Cd, As, Ni, and Cu in different rice varieties.

[0062]

[0063]

[0064] Feasibility of Standardizing Detection Methods for the Bioavailability of Cadmium, Arsenic, Nickel and Copper in Rice The optimized biocompatibility digestion model for cadmium, arsenic, nickel, and copper in rice was developed as follows: dissolution of cadmium, arsenic, nickel, and copper from rice was performed at a gastric juice pH of 1.6, a gastric digestion time of 1.5 h, an intestinal juice pH of 6.5, an intestinal digestion time of 3.0 h, a liquid-to-solid ratio of 150:1, and a bile salt concentration of 2.5 g / L. This model fully incorporates human physiological characteristics and is simple and easy to implement. Compared with existing in vitro digestion models, this method increases the efficiency of cadmium, arsenic, nickel, and copper biocompatibility detection; it also eliminates the oral digestion step and shortens the gastrointestinal digestion stage, thus reducing the overall experimental cycle. While saving time and costs, this method provides a new approach for more objectively and scientifically assessing the health risks of heavy metals cadmium, arsenic, nickel, and copper in rice to humans, and provides a basis for formulating scientific and reasonable heavy metal limits.

[0065] The embodiments described above can be further combined or replaced, and these embodiments are merely descriptions of preferred embodiments of the present invention, not limitations on the concept and scope of the present invention. Various changes and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the inventive concept are all within the protection scope of the present invention. The protection scope of the present invention is given by the appended claims and any equivalents.

Claims

1. An optimized method for simultaneous determination of the bioavailability of cadmium, arsenic, nickel, and copper in rice, characterized in that, Includes the following steps, S1. Randomly collect rice contaminated with heavy metals, and after processing and cooking, determine the total amount of cadmium, arsenic, nickel and copper in the rice. S2. Compare the effects of different in vitro digestion models on the bioavailability of cadmium, arsenic, nickel and copper in rice, and screen out the optimal in vitro digestion model. S3. Based on the optimal in vitro digestion model, the effects of liquid-to-solid ratio, gastric juice pH, gastric digestion time, intestinal juice pH, intestinal digestion time, and bile salt concentration on the bioavailability of cadmium, arsenic, nickel, and copper were determined by single-factor experiments. S4. Based on the results of the single-factor experiment, design a Plackett-Burman screening experiment to determine the significant factors, and conduct a Box-Behnken response surface experiment based on the significant factors. S5. Fit the response surface experimental results data to construct an in vitro digestion prediction model, and use the expected value function method to perform multi-objective global optimization with the goal of maximizing the bioavailability of four heavy metals simultaneously, and determine the optimal digestion conditions.

2. The optimization method as described in claim 1, characterized in that, In step S1, the processing and cooking conditions are as follows: milling precision 10%, washing twice, rice-to-water ratio 1:1.3, soaking for 0.5 h, and cooking at normal pressure.

3. The optimization method as described in claim 1, characterized in that, In step S2, the different in vitro digestion models include PBET, IVG, UBM, and INFOGEST.

4. The optimization method as described in claim 1, characterized in that, In step S4, a three-factor, three-level response surface methodology is performed using gastric juice pH, gastric digestion time, and intestinal digestion time. The gastric juice pH is 1.0, 1.5, and 2.0; the gastric digestion time is 1.0, 1.5, and 2.0 h; and the intestinal digestion time is 2.0, 3.0, and 4.0 h.

5. The optimization method as described in claim 1, characterized in that, The construction process of the prediction model also includes: S5, performing interaction factor effect analysis, optimization verification, expected value analysis, and result quality control on the cadmium, arsenic, nickel, and copper bioavailability model equation.

6. The optimization method as described in claim 1, characterized in that, The optimal digestion conditions are: liquid-to-solid ratio 150:1, gastric juice pH 1.6, gastric digestion time 1.5 h, intestinal juice pH 6.5, intestinal digestion time 3.0 h, and bile salt concentration 2.5 g / L.

7. An in vitro digestion method for simultaneously determining the bioavailability of cadmium, arsenic, nickel, and copper in rice, characterized in that, Based on the optimization method described in claim 1, the following steps are included: (a) Standardized samples were obtained by milling, washing, cooking and freeze-drying rice samples; (b) The sample was mixed with simulated gastric fluid at a liquid-to-solid ratio of 150:1 and digested with shaking at 37°C for 1.5 hours under pH 1.6 conditions; (c) Add simulated intestinal fluid to the digestion solution obtained in step (b), adjust the pH to 6.5, and the bile salt concentration to 2.5 g / L. Digest at 37°C with shaking for 3.0 hours. (d) After digestion, centrifuge and filter, and determine the concentrations of cadmium, arsenic, nickel and copper in the digestate; (e) Calculate the bioavailability of the four heavy metals based on the ratio of heavy metal content in the digestive fluid to the total content in the rice sample; The method does not include an oral digestion stage.

8. The method as described in claim 1, characterized in that, The pretreatment of the rice sample included: milling with a precision of 10%, washing twice, rice-to-water ratio of 1:1.3, soaking for 0.5 hours, and then steaming at normal pressure.

9. The method as described in claim 1, characterized in that, The simulated gastric juice contains 1.25 g / L pepsin, 0.5 g / L sodium malate, 0.5 g / L sodium citrate, 420 mL / L lactic acid, and 500 μL / L acetic acid; the simulated intestinal juice contains 2.5 g / L bile salts and 0.5 g / L pancreatic enzymes, but does not contain lipase or bovine serum albumin.

10. The method of any one of claims 7-9, in the application of assessing the health risks of heavy metals in rice or establishing food safety limits.

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