A method for determining multi-elements in cereals and their products by super-microwave digestion-ICP-OES internal standard method

Abstract

The application discloses a method for determining multi-elements in grains by super microwave digestion-ICP-OES internal standard method, which comprises the following steps: S1, mixing grains samples with acid, and performing super microwave digestion to obtain a digestion solution; S2, adding an internal standard solution to the digestion solution, and determining the volume to obtain a to-be-detected solution; S3, preparing an element standard solution, wherein the element standard solution contains an internal standard element; S4, determining a series of mixed element standard solutions by using an inductively coupled plasma emission spectrometer, and establishing standard curves of various elements; and S5, determining the to-be-detected solution by using the inductively coupled plasma emission spectrometer, and calculating the content of various elements in the sample according to the standard curves. The application realizes the objectivity and standardization of internal standard selection, significantly improves the accuracy and reliability of synchronous analysis of constant and trace elements in grains, and especially improves the problem of insufficient correction of some elements by a traditional single internal standard.

Description

Technical Field

[0001] This invention belongs to the field of analytical chemistry technology, specifically relating to a method for determining multiple elements in grains and their products using a super microwave digestion-ICP-OES internal standard method. Background Technology

[0002] Grains and their products (such as rice, millet, wheat, and corn) are the main energy source in the human diet. Their mineral content is relevant not only to nutritional value evaluation (e.g., essential elements like potassium, calcium, magnesium, iron, and zinc) but also to food safety risk monitoring (e.g., heavy metal contaminants like lead, cadmium, arsenic, and mercury). Therefore, establishing an efficient, accurate, and high-throughput method for simultaneous multi-element detection is of great significance for ensuring grain quality and safety and guiding residents towards a balanced diet.

[0003] Currently, commonly used methods for elemental detection include atomic absorption spectrometry (AAS), atomic fluorescence spectrometry (AFS), inductively coupled plasma optical emission spectrometry (ICP-OES), and inductively coupled plasma mass spectrometry (ICP-MS). Among these, ICP-OES is widely used in elemental analysis in food, environmental, and biological fields due to its advantages such as wide linear range, high sensitivity, simultaneous determination of multiple elements, and relatively low operating cost. In ICP-OES analysis, matrix effects are one of the key factors affecting detection accuracy. The internal standard method, as an effective interference correction technique, involves adding a known concentration of internal standard element to the sample and standard solution. Correction is achieved by utilizing the synchronous change in the signal response of the internal standard element and the target element, effectively reducing the influence of matrix effects and instrument signal drift.

[0004] Traditional ICP-OES internal standard methods typically employ two approaches to select internal standards: first, selecting elements with ionization and excitation energies similar to the target element based on experience; second, using a single universal internal standard (such as yttrium, bismuth, or indium) for uniform calibration of all target elements. However, the elemental content in grains and their products varies widely (from mg / kg level macroelements to μg / kg level trace elements), and the atomization, excitation, and ionization behaviors of different elements differ. Furthermore, the degree and direction of matrix effects (signal suppression or enhancement) vary significantly among different elements. A single internal standard cannot adequately compensate for the diverse matrix interferences faced by all target elements, leading to systematic biases in the detection results of some elements. For example, in practical analyses, when using a single internal standard for calibration, elements such as nickel, arsenic, phosphorus, and rubidium often exhibit severely deviated recoveries (e.g., recoveries as low as 71% or as high as 124%), indicating that existing internal standard selection methods lack fundamental and systematic calibration capabilities. For multi-element simultaneous analysis methods involving dozens of target elements, there are numerous candidate internal standard spectral lines (such as multiple spectral lines for elements like Ge, Re, Y, Bi, In, and Rh), and each candidate internal standard has varying correction effects for different elements. Traditional methods rely on analysts testing and comparing these lines manually based on experience, which is not only time-consuming and labor-intensive, but also highly susceptible to subjective factors, making it difficult to establish a standardized and reproducible screening process. When the detection matrix changes (e.g., from rice to wheat), the selection of internal standards needs to be manually readjusted, resulting in poor method transferability. Summary of the Invention

[0005] The purpose of this invention is to provide an accurate, efficient, and applicable method for the determination of multiple elements in grains and their products using the super microwave digestion-ICP-OES internal standard method by constructing an internal standard screening method that integrates analytical chemical performance indicators with a multi-indicator decision model and combining it with super microwave digestion technology.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A method for determining multiple elements in cereals and their products using super microwave digestion-ICP-OES internal standard method includes the following steps:

[0008] S1: Mix the grain sample with acid and perform super microwave digestion to obtain a digestion solution;

[0009] S2: Add internal standard solution to digestion solution, make up to volume, and obtain test solution;

[0010] S3: Prepare elemental standard solutions, wherein the elemental standard solutions contain internal standard elements;

[0011] S4: A series of mixed elemental standard solutions were determined using inductively coupled plasma atomic emission spectrometry, and standard curves for each element were established.

[0012] S5: The content of each element in the sample is calculated based on the standard curve using an inductively coupled plasma atomic emission spectrometer to determine the content of the test liquid.

[0013] The method further includes an internal standard spectral line screening step, which includes:

[0014] (a) For candidate internal standard spectral lines, obtain the analytical chemical performance index data of each candidate internal standard spectral line in the determination of target elements, wherein the analytical chemical performance index includes at least spike recovery rate, standard deviation of recovery rate, relative error and relative standard deviation;

[0015] (b) A multi-index decision algorithm is used to comprehensively evaluate the analytical chemical performance index data, and the optimal internal standard spectral line for the target element is selected based on the evaluation results;

[0016] (c) Based on the selected optimal internal standard spectral lines, an internal standard calibration system for the determination of multiple elements in cereals and their products is constructed.

[0017] The multi-index decision-making algorithm includes the CRITIC-TOPSIS algorithm;

[0018] The comprehensive evaluation includes:

[0019] (b1) The analytical chemical performance data are standardized, wherein the average spiked recovery rate, an intermediate indicator, is standardized with 100% as the ideal value; the standard deviation of recovery rate, relative error and relative standard deviation, three negative indicators, are negatively standardized.

[0020] (b2) The objective weights of each analytical chemical performance index were calculated using the CRITIC method;

[0021] (b3) Introduce the objective weight into the TOPSIS method, calculate the relative closeness of each candidate internal standard spectral line, and sort each candidate internal standard spectral line according to the relative closeness.

[0022] The standardization of average spiked recovery rate is performed using the following formula:

[0023] (1);

[0024] The standardization of negative indicators is performed using the following formula:

[0025] ;

[0026] In the formula, X ij The data is after normalization; x j For all original data of the j-th indicator; x ijFor the j-th index of the i-th internal standard element; X bestj The ideal value for the j-th index; This represents the maximum absolute value of the difference between the original data of the j-th indicator and the ideal value across all samples.

[0027] The calculation of the objective weights includes:

[0028] Calculate the variability of each indicator. ;

[0029] Calculate the conflict of each indicator ;

[0030] In the formula, rkj is the Pearson correlation coefficient between the k-th indicator and the j-th indicator. Let be the mean of the j-th indicator across all samples; k Let m be the mean of the k-th indicator across all samples; m be the number of samples; and n be the number of indicators. ;

[0031] Calculate information content (6);

[0032] Calculate weights .

[0033] The internal standard spectral line screening step is performed in or before step S4.

[0034] In one preferred embodiment, the grains and their products include at least one of rice, millet, corn, Job's tears, black rice, oats, quinoa, soybeans, wheat flour, and glutinous rice flour.

[0035] In one preferred embodiment, the grains and their products contain one or more of the following elements: Ca, Mg, K, Na, P, S, Fe, Zn, Al, As, B, Ba, Be, Cd, Co, Cr, Cu, Li, Mn, Mo, Ni, Pb, Rb, Sb, Se, Sn, Sr, Ti, Tl, and V.

[0036] In one preferred embodiment, the acid in step S1 is one or more of nitric acid, hydrochloric acid, and hydrofluoric acid.

[0037] In one preferred embodiment, the super microwave digestion in step S1 employs a two-step heating program; specifically: first, heating to 100℃ to 140℃ within 10 min to 20 min and holding for 25 min to 35 min; second, continuing to heat to 220℃ to 280℃ within 5 min to 15 min and holding for 15 min to 25 min.

[0038] In one preferred embodiment, the internal standard solution in step S2 is a stock solution containing an internal standard element, and the concentration of the internal standard solution is 100-1000 μg / mL.

[0039] In one preferred embodiment, the internal standard solution in step S2 is a solution containing Ge, Re, Y, Bi, In and Rh.

[0040] In one preferred embodiment, the internal standard element in step S3 is a combination of Ge, Re, and Y.

[0041] In one preferred embodiment, the candidate internal standard spectral lines in step (a) include one or more of the following: Bi 190.171 nm, Bi 222.821 nm, Bi 223.061 nm, Ge 206.866 nm, Ge 209.426 nm, Ge 219.871 nm, Ge 265.117 nm, In 230.606 nm, In 325.609 nm, Re 197.248 nm, Re 221.427 nm, Rh 233.477 nm, Rh 343.488 nm, Rh 369.236 nm, Y 324.228 nm, Y 360.074 nm, Y 371.029 nm, and Y 377.433 nm.

[0042] In one preferred embodiment, the calculation of the relative proximity of each candidate internal standard spectral line in step (b3) includes:

[0043] (b3-1) Constructing a weighted standardized decision matrix

[0044] The objective weight W obtained by the CRITIC method j Compared with standardized evaluation data X ij Multiplying them together yields the weighted standardized value z. ij This forms a weighted standardized decision matrix Z;

[0045] (8);

[0046] ;

[0047] (b3-2) Determine the positive ideal solution and the negative ideal solution

[0048] The ideal solution Z + The evaluation index is composed of the optimal weighted normalized value among all candidate internal standard spectral lines; that is, for each evaluation index, the maximum value of the weighted normalized value among all candidate internal standard spectral lines is taken. The negative ideal solution Z... -It consists of the worst weighted normalized value of each evaluation index among all candidate internal standard spectral lines, that is, for each evaluation index, the minimum weighted normalized value among all candidate internal standard spectral lines is taken;

[0049] ;

[0050] ;

[0051] (b3-3) Calculate the Euclidean distance

[0052] Calculate the Euclidean distance D between each candidate internal standard spectral line and the positive ideal solution. i + and the Euclidean distance D to the negative ideal solution. i - The Euclidean distance is calculated by summing the squares of the differences between the weighted standardized values ​​and the ideal values ​​for each evaluation index, and then taking the square root.

[0053] ;

[0054] ;

[0055] (b3-4) Calculate the relative proximity

[0056] Based on the distance D between each candidate internal standard spectral line and the positive ideal solution i + The distance D between the sum and the negative ideal solution i - Calculate the relative closeness C i ;

[0057] (14);

[0058] (b3-5) Ordering of internal standard spectral lines

[0059] Based on the calculated relative closeness C i The candidate internal standard spectral lines are sorted in descending order of their values.

[0060] In (b3-4), the relative closeness C i The value of C is between 0 and 1. i The closer the value is to 1, the better the overall correction performance of the candidate internal standard spectral line; C i The closer the value is to 0, the worse the overall correction performance.

[0061] In (b3-5), the higher the relative closeness, the better the overall correction effect of the internal standard spectral line after comprehensively considering the four evaluation indicators of spike recovery rate, recovery rate standard deviation, relative error and relative standard deviation.

[0062] In one preferred embodiment, the optimal internal standard spectral lines in step (c) are the following internal standard spectral lines: Ge 209.426 nm, Ge 219.871 nm, Ge 265.117 nm, Re 221.427 nm, Y 324.228 nm.

[0063] In one preferred embodiment, step (c) of constructing an internal standard calibration system for the determination of multiple elements in cereals and their products includes: extracting the top three candidate internal standard spectra in terms of relative proximity for each target element, counting the frequency of each candidate internal standard spectra in the top three among all target elements, determining the common internal standard spectra based on the frequency, and for target elements not effectively covered by the common internal standard spectra, selecting and supplementing internal standard spectra based on their relative proximity ranking to form a multi-internal standard combination calibration system.

[0064] In one preferred embodiment, the internal standard correction system is a combination of Ge, Re, and Y.

[0065] In one preferred embodiment, the internal standard correction system consists of the following internal standard spectral lines: Ge 209.426 nm, Ge 219.871 nm, Ge 265.117 nm, Re 221.427 nm, Y 324.228 nm.

[0066] Based on the same inventive concept, this invention also claims protection for the method for screening internal standard spectra for multi-element determination in grains and their products, comprising:

[0067] (a) For candidate internal standard spectral lines, obtain the analytical chemical performance index data of each candidate internal standard spectral line in the determination of target elements, wherein the analytical chemical performance index includes at least spike recovery rate, standard deviation of recovery rate, relative error and relative standard deviation;

[0068] (b) A multi-index decision algorithm is used to comprehensively evaluate the analytical chemical performance index data, and the optimal internal standard spectral line for the target element is selected based on the evaluation results;

[0069] (c) Based on the selected optimal internal standard spectral lines, an internal standard calibration system for the determination of multiple elements in cereals and their products is constructed, wherein the internal standard calibration system consists of internal standard spectral lines of Ge 209.426 nm, Ge 219.871 nm, Ge 265.117 nm, Re 221.427 nm, and Y 324.228 nm.

[0070] In one preferred embodiment, the multi-index decision algorithm includes the CRITIC-TOPSIS algorithm.

[0071] In one preferred embodiment, the comprehensive evaluation includes:

[0072] (b1) The analytical chemical performance data are standardized, wherein the average spiked recovery rate, an intermediate indicator, is standardized with 100% as the ideal value; the standard deviation of recovery rate, relative error and relative standard deviation, three negative indicators, are negatively standardized.

[0073] (b2) The objective weights of each analytical chemical performance index were calculated using the CRITIC method;

[0074] (b3) Introduce the objective weight into the TOPSIS method, calculate the relative closeness of each candidate internal standard spectral line, and sort each candidate internal standard spectral line according to the relative closeness.

[0075] The standardization of average spiked recovery rate is performed using the following formula:

[0076] (1);

[0077] The standardization of negative indicators is performed using the following formula:

[0078] ;

[0079] In the formula, X ij The data is after normalization; x j For all original data of the j-th indicator; x ij For the j-th index of the i-th internal standard element; X bestj The ideal value for the j-th index; This represents the maximum absolute value of the difference between the original data of the j-th indicator and the ideal value across all samples.

[0080] The calculation of the objective weights includes:

[0081] Calculate the variability of each indicator. ;

[0082] Calculate the conflict of each indicator ;

[0083] In the formula, rkj is the Pearson correlation coefficient between the k-th indicator and the j-th indicator. Let be the mean of the j-th indicator across all samples; k Let m be the mean of the k-th indicator across all samples; m be the number of samples; and n be the number of indicators. ;

[0084] Calculate information content (6);

[0085] Calculate weights .

[0086] Compared with the prior art, the beneficial effects of the present invention are:

[0087] (i) An innovative screening method integrating analytical chemistry performance indicators and multi-indicator decision-making models was constructed.

[0088] Existing ICP-OES internal standard methods typically rely on experience or single physicochemical parameters (such as ionization energy or spectral line type) for internal standard selection, making it difficult to systematically account for the complex matrix effect differences between elements in simultaneous multi-element analysis. This invention, for the first time, introduces the CRITIC-TOPSIS multi-index decision algorithm into the internal standard spectral line selection process, constructing a four-dimensional comprehensive evaluation system with spike recovery, recovery standard deviation, relative error, and relative standard deviation as core evaluation indicators. By differentiating between "intermediate indicators" and "negative indicators" through standardized processing, objective weight allocation based on variability and conflict, and comprehensive ranking to approximate the ideal solution, the objectivity and systematization of internal standard spectral line selection are achieved, resolving the technical contradiction of "difficulty in balancing accuracy and precision" in traditional methods.

[0089] (ii) A streamlined and efficient multi-internal standard combination calibration system was established.

[0090] In existing technologies, a single internal standard is insufficient to evenly compensate for the diverse matrix effects and instrument fluctuations faced by all analytes, leading to significant deviations in the detection results for some elements (such as Ni, As, P, and Rb). This invention utilizes a Top 3 frequency statistical strategy to transform the dispersed optimal solution output by the mathematical model into a combination of internal standards that meets actual detection requirements. Ultimately, five internal standard spectral lines—Ge 209.426 nm, Ge 219.871 nm, Ge 265.117 nm, Re 221.427 nm, and Y 324.228 nm—were selected, achieving efficient coverage of 31 target elements.

[0091] (III) High-throughput simultaneous detection of major and trace elements was achieved.

[0092] This invention combines super microwave digestion technology with the aforementioned multi-internal standard calibration method to establish a high-throughput detection method covering 31 elements (including macroelements Ca, Mg, K, Na, P, S, and trace elements Al, As, B, Ba, Be, Cd, Co, Cr, Cu, Fe, Li, Mn, Mo, Ni, Pb, Rb, Sb, Se, Sn, Sr, Si, Ti, Tl, V, and Zn). Method validation results show that the limits of detection for each element are 0.0029 mg / kg to 2.3904 mg / kg, the spiked recoveries are 87.7% to 110.4%, and the precision RSD is between 0.1% and 7.5%. Accuracy, precision, and recovery all meet the requirements.

[0093] (iv) Significantly improved detection efficiency and operational safety

[0094] This invention employs super microwave digestion technology, combining dynamic pressure regulation with a stepped heating process to achieve complete sample digestion in a short time, with low reagent consumption and minimal risk of elemental loss. Compared to traditional hot plate digestion or conventional microwave digestion, this method is simpler, safer, and more labor-saving, meeting the needs for rapid detection of large batches of grain samples.

[0095] (v) Possesses good practical application value

[0096] This invention has been successfully applied to the determination of actual samples from 10 different grain matrices (including rice, millet, corn, Job's tears, black rice, oats, quinoa, soybeans, wheat flour, and glutinous rice flour). The accuracy of the method has been verified using national standard reference materials (GBW10185 for rice and GBW10189 for millet), confirming its universality and reliability across different matrices. Furthermore, based on the detection results of this method, further research has been conducted on grain nutritional quality evaluation and heavy metal exposure risk assessment, providing reliable technical support for the quality control and food safety supervision of grains and their products.

[0097] In summary, this invention first utilizes a super microwave digester, combined with a specific microwave heating program, to completely digest the sample solution with nitric acid. The specific heating program of the super microwave digester, combined with nitric acid dissolution, solves the problem of traditional microwave digestion's inability to completely decompose insoluble substances and the elemental loss caused by hotplate digestion. The digested sample solution is then diluted using an appropriate gradient, and finally, the content of each element in the sample is measured using inductively coupled plasma atomic emission spectrometry (ICP-AES). This achieves safe, rapid, and accurate determination of the content of multiple elements. The application of super microwave instruments simplifies, ensures safety and speed in sample digestion, and reduces manpower requirements. Super microwave digestion solves operational safety issues in terms of time control and sample processing, avoids errors caused by incomplete sample dissolution and filtration, and saves operation time. The ICP instrument quantitatively improves the instability of titration operations and eliminates the complex procedures of calibration and temperature control. It solves the problems of cumbersome digestion and insufficient measurement range. Attached Figure Description

[0098] Figure 1 This is a bar chart showing the calculated matrix effects of elements such as As, Cd, Mg, Pb, P, Sb, and Sn.

[0099] Figure 2 It is a distribution map of the matrix effect differences of 31 elements under different elemental spectral lines.

[0100] Figure 3This is a bar chart showing the frequency of each internal standard spectral line among the top 3 best internal standards of 31 elements to be measured. Detailed Implementation

[0101] This invention is not limited to the specific embodiments listed below. Those skilled in the art can implement this invention using various other specific embodiments based on the content disclosed herein. Any modifications or alterations made to the design structure and concept of this invention fall within the protection scope of this invention. It should be noted that, unless otherwise specified, the embodiments and features described in this invention can be combined with each other.

[0102] This invention has validated its methodology and applied it to various real-world samples. The analytical performance of the method was validated through a comprehensive evaluation of accuracy, precision, and spiked recovery.

[0103] (1) Accuracy test: Select nationally certified standard materials and determine their multi-element content according to the method of this invention, and compare the consistency of the determination results with the reference values ​​of the standard reference materials.

[0104] (2) Recovery and Precision Tests: Select grain samples from the same batch (e.g., rice) and first determine the background values ​​of the target elements. Then, add mixed standard solutions of the elements at low, medium, and high concentration levels, and perform three parallel determinations at each spiking level. Calculate the relative standard deviation (RSD) of the results at each level to assess the method precision, and calculate the spiked recovery rate to assess the method accuracy. The recovery rates of each element at different spiking levels should be within the range of 85% to 115%, and their RSDs should meet the requirements of the corresponding standards (e.g., GB / T 27404-2008).

[0105] Example 1

[0106] Methods and Experimental Design

[0107] (1) Matrix effect quantification: The matrix effect (ME) of each element is calculated by the slope ratio method to clarify the necessity of internal standard correction.

[0108] (2) Selection of spectral lines for internal standard elements:

[0109] To evaluate the correction efficacy of internal standards in the analysis of actual samples, a spiked recovery experiment was conducted using rice samples. Four equal portions of sample were accurately weighed; one portion served as the background, and the other three portions were spiked with standard solutions of the target elements before microwave digestion. The spike levels were divided into three groups: Group 1 added Ca, K, Mg, Na, and P at a concentration of 250.0 mg / kg; Group 2 added Cu, Fe, Zn, and Rb at a concentration of 25.0 mg / kg; and Group 3 added As, Ba, Be, Cd, Co, Cr, Li, Ni, Pb, Se, Sr, Tl, V, Ti, Sn, B, Sb, Mo, Mn, and Al at a concentration of 2.5 mg / kg. All four groups of samples underwent the same microwave digestion and volume adjustment procedure, and were then analyzed using different internal standards via ICP-OES. The impact of the internal standard on analytical accuracy and reproducibility was comprehensively evaluated by calculating the spiked recoveries of each element and the standard deviations of six parallel determinations.

[0110] To systematically evaluate the matrix effect of coexisting elements in cereal acid digestion on the signal stability of ICP-OES determination, this invention designs an experimental system simulating a real sample matrix. A 5% HNO3 solution containing 5.0 mg / L Na and K is used as the basic matrix to simulate a background of high concentrations of easily ionized elements. Based on this, three types of test solutions were prepared: (1) 22 trace and ultra-trace elements (0.1 mg / L each) and internal standards were added to the basic matrix to examine the signal stability of low-concentration analytes in the background of high Na and K; (2) Ca, Si, Mg, K, Na, P, S, Fe, and Zn (5.0 mg / L each) and internal standards were added to evaluate the physical and spectral interference that may be caused by constant coexisting elements; (3) The basic matrix containing only 5.0 mg / L Na and K was used as a blank reference solution to monitor the Na and K signals themselves and evaluate the correction effectiveness of the internal standard in the strongly ionized interference matrix, providing a matrix matching benchmark for the whole analysis.

[0111] The method for determination using ICP-OES is as follows:

[0112] S1: Place 0.5000 g of the sample to be tested and 2.5 mL of nitric acid into the digestion tube in sequence. After placing it on the support and sealing it, place it in a nitrogen-sealed reactor containing 150 mL of deionized water and 5 mL of nitric acid for super microwave digestion. After digestion, collect the solution, rinse the inner cap and transfer the mixture to a 50 mL volumetric flask. Add internal standard and make up to volume. Shake well and process blank samples simultaneously.

[0113] S2: Heating steps in the super microwave digestion process:

[0114]

[0115] S3: Preparation of sample test solution: After passing the sample solution from S1 through S2 and cooling, transfer it to a 50 mL volumetric flask, add the internal standard element, dilute to volume, shake well, and wait for instrumentation. Prepare a blank sample at the same time.

[0116] S4: ICP instrument setup and preparation of mixed elemental standard solutions:

[0117] (1) The test conditions for the elements were set according to the instruction manual of the ICP analyzer (Agilent 5800), including gas flow rate, analytical spectral lines, calculation model, signal-to-noise ratio, background interference elimination, etc. The optimized conditions were: RF power: 1.25 kW; plasma gas: 12.0 L / min; auxiliary gas: 1.0 L / min; nebulizer gas: 0.7 L / min; observation mode: radial / axial (selected according to element sensitivity); pump speed: 12 r / min.

[0118] (2) Preparation of mixed element standard solutions: A series of mixed standard working solutions are prepared by step dilution with an appropriate amount of nitric acid solution (such as 5% HNO3). The specific concentration gradients are as follows: For As, Ba, Be, Cd, Co, Cr, Ni, Pb, Se, Sr, Tl, V, Ti, Sn, B, Sb, Mo, Mn, Cu, and Li, the standard solution concentration gradients are 0, 1.0, 2.0, 5.0, 10.0, 20.0, 50.0, 100.0, 200.0, 300.0, and 500.0 ug / L; for Al and Rb, the standard solution concentrations are 0, 0.1, 0.5, 1.0, 2.0, and 5.0 mg / L; and for Ca, Si, Mg, K, Na, P, S, Fe, and Zn, the standard solution concentration gradients are 0, 0.1, 0.5, 1.0, 3.0, 5.0, 10.0, 20.0, and 40.0 mg / L, with a quantitative addition of 100 mg / L. Add 0.5 mL each of the internal standard solutions of germanium (Ge), rhenium (Re), bismuth (Bi), indium (In), yttrium (Y), and rhodium (Rh) at a concentration of μg / mL, bring the volume to 50.0 mL, and vortex for 30 s to mix.

[0119] S5: Elemental spectral line selection: By systematically evaluating the matrix effects (such as signal suppression / enhancement) and the correction performance of internal standard elements (germanium (Ge), rhenium (Re), bismuth (Bi), indium (In), yttrium (Y) and rhodium (Rh)), the optimal combination of analytical wavelength and internal standard is selected.

[0120] The selection of the optimal internal standard is based on actual sample spiked recovery test data (calculating recovery rate and standard deviation) supplemented by simulated matrix interference test data (calculating relative error and relative standard deviation), and is completed by comprehensively comparing these indicators. The calculation formula is as follows:

[0121] Spiked recovery rate Rec: .

[0122] Recovery rate standard deviation (SD): .

[0123] Relative error re: .

[0124] Relative standard deviation rsd: .

[0125] In the formula, The recovery rate is the rate measured in a single study. The average recovery rate is given by n, where n is the number of determinations. and These are the average and standard deviation of the concentrations of the target element, respectively. This is the theoretical concentration value.

[0126] The comprehensive ranking is carried out according to the following process: (a) Determine the weights based on the CRITIC method: Calculate the objective weights of each evaluation index using the CRITIC method. (b) Determine the ranking based on the TOPSIS method: Combine the obtained index weights and use the TOPSIS method to comprehensively evaluate and rank all candidate internal standard spectral line combinations, and select the optimal internal standard spectral line accordingly.

[0127] 2. Experimental Results and Screening Conclusions

[0128] (1) Assess matrix effects

[0129] The matrix effect is evaluated by the ratio of the slope of the matrix standard curve to the slope of the solvent standard curve. For example... Figure 1 As shown, the matrix effect on elements such as As, Cd, Mg, Pb, P, Sb, and Sn all exhibits signal suppression (ME < -20%), while the matrix effect on Cr and Rb both exhibits signal enhancement (ME > 20%). This diametrically opposed phenomenon stems from the multi-level physicochemical interactions between the complex matrix and the plasma, as well as the differences in the spectroscopic properties of the analytes themselves. It also indicates that the degree of matrix influence varies significantly among different elements, further highlighting the necessity of selecting optimal internal standards for each element.

[0130] Signal suppression primarily stems from two main mechanisms: First, for elements such as As, Cd, and Pb, their commonly used analytical lines are mostly ionic lines. The presence of high concentrations of easily ionized elements (EIEs) alters the ionization equilibrium of the plasma, inhibiting the transformation of these elements from the atomic state to the ionic state, thus leading to a significant decrease in the ionic line signal. Second, a high-salt matrix alters the physical properties of the solution, reducing nebulization transmission efficiency; this physical effect causes widespread signal suppression for all elements.

[0131] In contrast, the signal enhancement of Cr and Rb reveals another aspect of matrix effects. The enhancement of Rb mainly stems from the excitation-enhancing effect on its atomic lines in the presence of EIEs. The signal enhancement mechanism of Cr is more complex; one possibility is an increase in sample introduction efficiency rather than changes within the plasma. Furthermore, enhancement effects in specific energy ranges or spectral interferences cannot be ruled out. Therefore, using the internal standard method to correct for matrix effects is necessary. The internal standard method introduces a reference element with similar physicochemical behavior to the target element to achieve dynamic correction of multi-dimensional matrix interferences. Its core advantage lies in simultaneously compensating for atomization transmission fluctuations, ionization suppression or enhancement, and chemical complexation effects.

[0132] In ICP-OES analysis, germanium (Ge), rhenium (Re), bismuth (Bi), indium (In), yttrium (Y), and rhodium (Rh) were selected as candidate internal standards to correct for signal drift and matrix effects, thereby improving the accuracy of quantitative analysis. Figure 2 As shown, elements such as germanium (Ge) and yttrium (Y) exhibited good correction effects, confirming the effectiveness of the internal standard method in compensating for matrix effects and suppressing signal drift. Preliminary evaluations showed that the matrix effects of the spectral lines of Bi (190.171 nm, 222.81 nm, 223.061 nm), Ge (206.866 nm, 209.426 nm, 219.871 nm, 265.117 nm), In (230.606 nm, 325.609 nm), Y (324.228 nm, 360.074 nm, 371.029 nm, 377.433 nm), Re (197.248 nm, 221.427 nm), and Rh (233.477 nm, 343.488 nm, 369.236 nm) were all low. However, how to scientifically select a single, optimal internal standard from numerous candidate spectral lines to match a specific analyte remains a core challenge for traditional methods.

[0133] Generally, in emission spectroscopy analysis, an ideal internal standard element should have a similar ionization energy, excitation energy, and the same spectral line type (atomic or ionic lines) as the analyte. Table 2 shows the preliminary matching results for all 31 target elements based on this principle. The results show that even following this principle, a complex internal standard system consisting of more than ten spectral lines still needs to be constructed. Furthermore, this method relies on only a single physical parameter and fails to comprehensively consider key performance aspects such as accuracy and precision in actual analysis. Therefore, this study will subsequently conduct spike recovery experiments and combine them with the CRITIC-TOPSIS decision model to perform a multi-dimensional objective comprehensive evaluation and ranking of candidate spectral lines, aiming to select the internal standard scheme with the best overall performance.

[0134]

[0135] (2) Determining weights based on the CRITIC method

[0136] Standardization of raw content data

[0137] To comprehensively evaluate the applicability of 19 internal standard correction schemes (including schemes without internal standards) to all 31 target elements, this study adopted a stratified evaluation strategy. For each target element, experimental data were evaluated based on its spiking experiment and its accuracy and precision. Taking aluminum as an example, its original evaluation data matrix is ​​shown in Table 3.

[0138]

[0139] A quality evaluation dataset was established for the content of four indicators among the 19 internal standard elements and standardized. The average recovery rate of intermediate indicators was processed using formula (1), and the negative indicators (standard deviation of recovery rate, relative standard deviation, and relative error) were processed using formula (2). There are n samples to be evaluated, i.e., evaluation objects, and each sample to be evaluated has m evaluation indicators.

[0140] (1).

[0141] .

[0142] In the formula, X ij The data is after normalization; x j For all original data of the j-th indicator; x ij For the j-th index of the i-th internal standard element; X bestj The ideal value for the j-th index; This represents the maximum absolute value of the difference between the original data of the j-th indicator and the ideal optimal value in all samples. A quality evaluation dataset is established using the content of four indicators from 19 internal standard correction scenarios. The internal standard elements are matrix columns, and the experimental data are matrix rows. The variability (S) is calculated according to formula (3). j ), and use formulas (4) and (5) to calculate the conflict (R) j Then, based on the public notices (6) and (7), calculate the information content (C). j ) and weight (W) j ).

[0143] ;

[0144] In the formula, S j Let be the variability of the j-th index; j Let be the mean of the j-th indicator across all samples;

[0145] ;

[0146] In the formula, r kj Let Pearson's correlation coefficient be the sum of the k-th and j-th indicators. k is the mean of the k-th indicator across all samples; m is the number of samples; n is the number of indicators.

[0147] ;

[0148] (6);

[0149] .

[0150] The CRITIC method was used to objectively assign weights to the four core evaluation indicators, and the results are shown in Table 4. This method determines the comprehensive weight by quantifying the degree of variation of each indicator and the correlation between them. The calculation results show that the average spike recovery rate (%) has the highest weight, ranging from 0.69 (Na) to 0.94 (Rb), with a mean of 0.82, which is significantly higher than indicators such as the relative standard deviation (RSD) characterizing precision and the relative error (RE) characterizing the level of systematic error control.

[0151]

[0152] (3) Determine the ranking based on the TOPSIS method

[0153] To comprehensively evaluate the correction effects of different internal standard element spectral lines, this study introduces the CRITIC-TOPSIS method (a method for ranking approximate ideal solutions based on CRITIC weights) for integrated analysis. This method ranks the schemes by calculating their relative closeness to the positive and negative ideal solutions. The weights W obtained using the CRITIC method are... j The standardized evaluation matrix is ​​weighted to determine the positive ideal solution Z for each indicator. + With negative ideal solution Z - Subsequently, the Euclidean distance formula was used to calculate the distance D between each internal standard case and the positive ideal solution. i + Distance Di- and relative proximity C of the negative ideal solution i The values ​​are shown in Table 5.

[0154] (8).

[0155] .

[0156] In the formula, z ij Z is the weighted standardized value of the j-th indicator of the i-th internal standard, and Z is the weighted standardization matrix.

[0157] ; ;

[0158] In the formula, Z + The set of optimal weighted values ​​for each indicator.

[0159] ;

[0160] In the formula, Z - This is the set of worst-case weighted values ​​for each indicator.

[0161] Calculate the Euclidean distance between the spectral lines of each internal standard element and the positive and negative ideal solutions:

[0162] ;

[0163] ;

[0164] (14);

[0165] In the formula, The distance from the i-th internal index to the positive ideal solution The distance from the i-th internal index to the negative ideal solution.

[0166] Relative closeness C i It reflects how closely the internal standard matches the ideal solution; the closer it is to 1, the better the fit.

[0167] Table 5 lists the relative proximity rankings calculated using the CRITIC-TOPSIS method, intuitively reflecting the comprehensive correction effect of each internal standard line for a specific analyte. However, the optimal internal standard lines for a single analyte are often highly dispersed, making them difficult to use directly for simultaneous multi-element analysis.

[0168]

[0169] Therefore, this study introduces a secondary statistical strategy: first, the top three (Top 3) internal standard spectral lines for each element to be tested are extracted, and then the frequency of each spectral line entering the Top 3 is counted (see...). Figure 3 This frequency analysis identifies high-performance internal standard lines with broad applicability. In practical applications, these high-spectral lines are preferred. If they cannot completely cover all elements, suboptimal lines are selected as supplements within a limited range based on the results in Table 5, ultimately forming a concise and efficient internal standard combination scheme (see Table 6).

[0170]

[0171] Note: The bolded elements in the table indicate that the element to be measured is calibrated using the corresponding internal standard spectral line.

[0172] Statistical results show that Ge209.426, Ge265.117, Y324.228, and Re221.427 are the most frequently occurring internal standard lines, exhibiting superior overall applicability. The Ge209.426nm and Ge265.117nm lines also perform well; the former focuses on alkaline earth elements and transition metals, while the latter covers rare metals and half-metals. Y324.228 covers a diverse range of elements, including metals, half-metals, and non-metals, while Ge219.871 performs exceptionally well in the calibration of specific elements such as lithium, potassium, and copper. Meanwhile, the Re221.427nm line demonstrates outstanding performance across multiple indicators, frequently appearing in matching lists for various elements, showcasing broad applicability.

[0173] The Ge internal standard spectral lines selected in this study resonate well with previous literature reports. Barros et al. successfully achieved accurate correction for multiple elements, including Sb and Cr, using Ge 265.117 nm I as an internal standard in urine ICP-OES analysis, providing important evidence for the effectiveness of the Ge 265.117 nm spectral line in complex biological matrices. Although this study focused on a single spectral line and its elemental coverage differed from the 31 elements in this study, the consistency between the two studies regarding key elements such as Sb and Cr constitutes cross-matrix mutual verification. Based on this, this study further extends the examination of Ge internal standards from single-line to multi-line levels, systematically revealing the differentiated applicability of Ge 219.871 nm and Ge 209.426 nm, providing more comprehensive data support for the refined selection of internal standards in practical applications.

[0174] Example 2

[0175] To explore the performance and limitations of traditional internal standard methods in the analysis of complex samples, this study first evaluated two of the most commonly used single internal standard schemes, Y371.029 and Bi223.061. The procedure is as follows:

[0176] S1: Place 0.5000 g of the sample to be tested and 2.5 mL of nitric acid into the digestion tube in sequence. After placing it on the support and sealing it, place it in a nitrogen-sealed reactor containing 150 mL of deionized water and 5 mL of nitric acid for super microwave digestion. After digestion, collect the solution, rinse the inner cap and transfer the mixture to a 50 mL volumetric flask. Add internal standard and make up to volume. Shake well and process blank samples simultaneously.

[0177] S2: Heating steps of the super microwave digestion program: The heating program of super microwave digestion is divided into two steps: the first step is to heat up to 100℃ in 10 minutes and hold for 10 minutes after reaching the temperature; the second step is to heat up to 230℃ in 12 minutes and hold for 16 minutes after reaching the temperature, thus completing the entire digestion heating process.

[0178] S3: Preparation of sample test solution: After passing the sample solution from S1 through S2 and cooling, transfer it to a 50 mL volumetric flask, add the internal standard element, dilute to volume, shake well, and wait for instrumentation. Prepare a blank sample at the same time.

[0179] S4: ICP instrument setup and preparation of mixed elemental standard solutions:

[0180] (1) The test conditions for the elements were set according to the instruction manual of the ICP analyzer (Agilent 5800), including gas flow rate, analytical spectral lines, calculation model, signal-to-noise ratio, background interference elimination, etc. The optimized conditions were: RF power: 1.25 kW; plasma gas: 12.0 L / min; auxiliary gas: 1.0 L / min; nebulizer gas: 0.7 L / min; observation mode: radial / axial (selected according to element sensitivity); pump speed: 12 r / min.

[0181] (2) Preparation of mixed element standard solutions: A series of mixed standard working solutions are prepared by step dilution with an appropriate amount of nitric acid solution (such as 5% HNO3). The specific concentration gradients are as follows: For As, Ba, Be, Cd, Co, Cr, Li, Ni, Pb, Se, Sr, Tl, V, Ti, Sn, B, Sb, Mo, Mn, and Cu, the standard solution concentration gradients are 0, 1.0, 2.0, 5.0, 10.0, 20.0, 50.0, 100.0, 200.0, 300.0, and 500.0 μg / L; for Al and Rb, the standard solution concentrations are 0, 0.1, 0.5, 1.0, 2.0, and 5.0 mg / L; and for Ca, Si, Mg, K, Na, P, S, Fe, and Zn, the standard solution concentration gradients are 0, 0.1, 0.5, 1.0, 3.0, 5.0, 10.0, 20.0, and 40 mg / L, with a quantitative addition of 100 mg / L. Add 0.5 mL each of the internal standard solutions of germanium (Ge), rhenium (Re), bismuth (Bi), indium (In), yttrium (Y), and rhodium (Rh) at a concentration of μg / mL, bring the volume to 50.0 mL, and vortex for 30 s to mix.

[0182] The results, shown in Table 7, reveal significant differences and large fluctuations in the correction effects of the two internal standards for different elements. For example, Y internal standard performed better for elements such as K and Mn, while Bi internal standard was more suitable for elements such as Cu. More importantly, elements such as As, Ni, P, and Rb showed serious deviations in recovery rates and poor data precision under both single standards, with Ni recovery rates being low (approximately 71%–82%) and P recovery rates being high (approximately 119%–124%). These results indicate that relying on a single, universal internal standard cannot adequately compensate for the diverse matrix effects and instrument fluctuations faced by all analytes, revealing a fundamental and systematic deficiency in the correction capability of this strategy.

[0183]

[0184] Example 3

[0185] To verify the practical effectiveness of the proposed CRITIC-TOPSIS internal standard selection method, we compared it with two existing schemes. All experiments used the same sample pretreatment and instrument conditions as in Example 1. Scheme A employed the fixed internal standard combination reported in the literature "Rapid Atomic Emission Spectroscopy Analysis of Multiple Elements in Brown Rice," using Y371.029 to correct ion lines and Bi223.061 to correct atomic lines. Scheme B selected theoretically matched internal standards for each element based on the general principles of spectral line type and ionization potential matching (i.e., the matching results in Table 2). Scheme C, as described in this invention, uses the internal standard combination selected through the CRITIC-TOPSIS model. The process is as follows:

[0186] S1: Place 0.5000 g of the sample to be tested and 2.5 mL of nitric acid into the digestion tube in sequence. After placing it on the support and sealing it, place it in a nitrogen-sealed reactor containing 150 mL of deionized water and 5 mL of nitric acid for super microwave digestion. After digestion, collect the solution, rinse the inner cap and transfer the mixture to a 50 mL volumetric flask. Add internal standard and make up to volume. Shake well and process blank samples simultaneously.

[0187] S2: Heating steps of the super microwave digestion program: The heating program of super microwave digestion is divided into two steps: the first step is to heat up to 100℃ in 10 minutes and hold for 10 minutes after reaching the temperature; the second step is to heat up to 230℃ in 12 minutes and hold for 16 minutes after reaching the temperature, thus completing the entire digestion heating process.

[0188] S3: Preparation of sample test solution: After passing the sample solution from S1 through S2 and cooling, transfer it to a 50 mL volumetric flask, add the internal standard element, dilute to volume, shake well, and wait for instrumentation. Prepare a blank sample at the same time.

[0189] S4: ICP instrument setup and preparation of mixed elemental standard solutions:

[0190] (1) The test conditions for the elements were set according to the instruction manual of the ICP analyzer (Agilent 5800), including gas flow rate, analytical spectral lines, calculation model, signal-to-noise ratio, background interference elimination, etc. The optimized conditions were: RF power: 1.25 kW; plasma gas: 12.0 L / min; auxiliary gas: 1.0 L / min; nebulizer gas: 0.7 L / min; observation mode: radial / axial (selected according to element sensitivity); pump speed: 12 r / min.

[0191] (2) Preparation of mixed element standard solutions: A series of mixed standard working solutions are prepared by step dilution with an appropriate amount of nitric acid solution (such as 5% HNO3). The specific concentration gradients are as follows: For As, Ba, Be, Cd, Co, Cr, Ni, Pb, Se, Sr, Tl, V, Ti, Sn, B, Sb, Mo, Mn, Cu, and Li, the standard solution concentration gradients are 0, 1.0, 2.0, 5.0, 10.0, 20.0, 50.0, 100.0, 200.0, 300.0, and 500.0 ug / L; for Al and Rb, the standard solution concentrations are 0, 0.1, 0.5, 1.0, 2.0, and 5.0 mg / L; and for Ca, Si, Mg, K, Na, P, S, Fe, and Zn, the standard solution concentration gradients are 0, 0.1, 0.5, 1.0, 3.0, 5.0, 10.0, 20.0, and 40.0 mg / L, with a quantitative addition of 100 mg / L. Add 0.5 mL each of the internal standard solutions of germanium (Ge), rhenium (Re), bismuth (Bi), indium (In), yttrium (Y), and rhodium (Rh) at a concentration of μg / mL, bring the volume to 50.0 mL, and vortex for 30 s to mix.

[0192] The internal standard uses the spectral lines shown in Table 6.

[0193] The detailed analysis results are shown in Table 8.

[0194]

[0195] In terms of spiked recovery, Scheme C achieves a recovery rate closer to the ideal 100% for the vast majority of analytes. For example, Ni recovery is only 71.3% in Scheme A, 82.38% in Scheme B, but significantly improved to 102.82% in Scheme C; As recovery is 85.4% and 93.56% in Schemes A and B, respectively, while Scheme C reaches 99.39%. This indicates that, compared to a fixed combination of internal standards or internal standards based solely on theoretical matching principles, internal standards selected based on objective weighting of multiple indicators can more effectively correct for signal suppression or enhancement effects caused by the sample matrix, thus obtaining more accurate quantitative results.

[0196] Comparing the precision of the determination results, Scheme C showed comparable or better repeatability than Scheme A in most cases. For example, in the determination of Se, the RSD of Scheme C was 1.11%, slightly higher than that of Scheme B (1.30%), but its recovery rate (99.71%) was far superior to that of Scheme B (96.84%), maintaining good precision while ensuring accuracy. For some elements such as rubidium (Rb), although the RSD of Scheme C (2.88%) was still relatively high, it was significantly better than that of Scheme A (3.5%) and Scheme B (2.69%), indicating that Scheme C has an advantage in improving the stability of the results.

[0197] In terms of data dispersion and relative error, scheme C typically results in less data fluctuation and systematic error. Taking Fe as an example, scheme B exhibits a high SD of 29.49 and a RE of 0.03%, demonstrating exceptionally high data dispersion and extremely low relative error. This may indicate that while the internal standard selection may have accidentally led to the mean approaching the true value, the process is highly unstable. Scheme C, on the other hand, has an SD of 2.42 and a RE of 0.69%, ensuring both accuracy and higher data reliability. Similarly, for P, scheme C significantly reduces the recovery rate from 118.9% in scheme A and 116.93% in scheme B to 107.59%, while also achieving a better SD than scheme B, demonstrating a significant correction effect.

[0198] In summary, the advantages and disadvantages of the three schemes are clearly distinguishable. Scheme A is simple to operate, but its correction effect for Ni, As, and Na is poor. Scheme B is based on physicochemical principles, and its performance is sometimes better than Scheme A, such as with Se, but it may also introduce new unstable factors, such as Fe, resulting in inconsistent performance. Scheme C, on the other hand, is data-driven, comprehensively balancing multiple performance indicators such as recovery rate and precision, and selecting the internal standard with the best overall performance for each element under specific experimental conditions. Therefore, in the complex analysis of rice matrix, Scheme C demonstrates a more comprehensive and robust correction capability, and can improve the accuracy and reliability of simultaneous multi-element analysis overall.

[0199] Example 4

[0200] Based on the optimal internal standard combination of Ge219.871, Ge209.426, Ge265.117, Re221.427, and Y324.228 selected by the CRITIC-TOPSIS model, this study further established analytical methods for each element to be measured and systematically verified the performance of the methodology.

[0201] S1: Place 0.5000 g of the sample to be tested and 2.5 mL of nitric acid into the digestion tube in sequence. After placing it on the support and sealing it, place it in a nitrogen-sealed reactor containing 150 mL of deionized water and 5 mL of nitric acid for super microwave digestion. After digestion, collect the solution, rinse the inner cap and transfer the mixture to a 50 mL volumetric flask. Add internal standard and make up to volume. Shake well and process blank samples simultaneously.

[0202] S2: Heating steps of the super microwave digestion program: The heating program of super microwave digestion is divided into two steps: the first step is to heat up to 100℃ in 10 minutes and hold for 10 minutes after reaching the temperature; the second step is to heat up to 230℃ in 12 minutes and hold for 16 minutes after reaching the temperature, thus completing the entire digestion heating process.

[0203] S3: Preparation of sample test solution: After passing the sample solution from S1 through S2 and cooling, transfer it to a 50 mL volumetric flask, add internal standard elements Ge, Re, and Y, and make up to volume. The concentration of the internal standard elements is 1 mg / L. Shake well and wait for instrumentation. Prepare blank samples simultaneously.

[0204] S4: ICP instrument setup and preparation of mixed elemental standard solutions:

[0205] (1) The test conditions for the elements were set according to the instruction manual of the ICP analyzer (Agilent 5800), including gas flow rate, analytical spectral lines, calculation model, signal-to-noise ratio, background interference elimination, etc. The optimized conditions were: RF power: 1.25 kW; plasma gas: 12.0 L / min; auxiliary gas: 1.0 L / min; nebulizer gas: 0.7 L / min; observation mode: radial / axial (selected according to element sensitivity); pump speed: 12 r / min.

[0206] (2) Preparation of mixed element standard solutions: A series of mixed standard working solutions are prepared by step dilution with an appropriate amount of nitric acid solution (such as 5% HNO3). The specific concentration gradients are as follows: Standard solutions of As, Ba, Be, Cd, Co, Cr, Ni, Pb, Se, Sr, Tl, V, Ti, Sn, B, Sb, Mo, Mn, Cu, and Li have concentration gradients of 0, 1.0, 2.0, 5.0, 10.0, 20.0, 50.0, 100.0, 200.0, 300.0, and 500.0 ug / L; Al and Rb standard solutions have concentrations of 0, 0.1, 0.5, 1.0, 2.0, and 5.0 mg / L; Ca, Si, Mg, K, Na, P, S, Fe, and Zn standard solutions have concentration gradients of 0, 0.1, 0.5, 1.0, 3.0, 5.0, 10.0, 20.0, and 40.0 mg / L. 5% (v / v) nitric acid solution is added, and 100 mg / L is added quantitatively. Add 0.5 mL each of germanium (Ge), rhenium (Re), yttrium (Y), and internal standard solution at a concentration of μg / mL to a final volume of 50.0 mL, and vortex for 30 s to mix thoroughly.

[0207] S5: Plotting the working curve: A working curve was plotted using the method described above to obtain the correlation coefficients of the elemental curves. Eleven blank solutions were prepared according to the experimental method and measured three times. The method detection limit was calculated using the detection limit formula defined by the International Union of Pure and Applied Chemistry (IUPAC): CL = 3Sb / k (where Sb is the standard deviation of the blank and k is the slope of the corresponding calibration curve). The data listed in Table 9 were calculated based on the sample pretreatment procedure (reducing a 0.5000 g sample to a final volume of 50 mL).

[0208]

[0209] S6: Methodological Validation: By executing the method shown in S5, determine the detection response value of the target element in the grain sample obtained after treatment in S3; based on the response value and the pre-established standard curve of the target element, calculate the content of the corresponding element in the grain sample. Verify the accuracy, precision, and recovery rate of the elemental content analysis method to determine its feasibility.

[0210] (1) Accuracy test

[0211] To verify accuracy, standard substances GBW10185 rice and GBW10189 millet were used in this experiment. The pretreatment method, instrument operating parameters, and working solution preparation were the same as steps S1-S5 in Example 4. After digestion, the standard substances were analyzed on the instrument in triplicate. The results are shown in Table 10. Among the two standard substances used for verification, the measured concentrations higher than the LOQ, except for silicon, all fell within the range of their respective certificate values, fully demonstrating the feasibility and reliability of this technique in multi-element analysis. It should be noted that the silicon measured in this experiment is "acid-soluble silicon" rather than "total silicon." Active silicon refers to the portion of silicon in plant tissue that exists in silicate form and can be digested by super microwave and completely dissolved by nitric acid.

[0212]

[0213] Note: Certificate values ​​are expressed in ± to indicate uncertainty; values ​​in parentheses are reference values.

[0214] As shown in Table 10, among the two standard substances tested, the measured concentrations higher than the LOQ, except for silicon, all fell within the range of their respective certificate values, fully demonstrating the feasibility and reliability of this technique in multi-element analysis. It should be noted that this experiment measured "digestible silicon" in plant tissue. Since super-microwave digestion cannot completely decompose all forms of silicon (such as crystalline silicates), the results reflect the silicon component present in the sample as amorphous silica (phytoliths). This differs from the total silicon reference value provided in the standard substance certificate, which is a normal phenomenon under different methodological definitions.

[0215] (2) Spike recovery test and precision test

[0216] Under the same experimental conditions, a spiked recovery test was conducted. Rice samples (approximately 0.4-0.5 g) were weighed, and before digestion, different amounts of multi-element mixed standard stock solution were precisely added. After digestion, the volume was adjusted to 50 mL to prepare spiked simulated extracts at low, medium, and high concentration levels. The concentration increments resulting from the spikes were as follows: As, B, Ba, Be, Cd, Co, Cr, Cu, Fe, Li, Mn, Mo, Ni, Pb, Rb, Sb, Se, Sn, Sr, Ti, Tl, V: 0.005 µg / mL, 0.01 µg / mL, 0.1 µg / mL; Al, Na, Zn: 0.1 µg / mL, 0.5 µg / mL, 1 µg / mL; Ca, S, Si, K, Mg, P: 0.5 µg / mL, 1 µg / mL, 10 µg / mL. Based on the conversion between the fixed volume and the sample mass, this concentration increment is equivalent to adding approximately 11.9 mg / kg (low), 54.04 mg / kg (medium), and 116.43 mg / kg (high) spiking amounts to the sample (taking Al as an example; the same applies to other elements). The recovery rate and RSD of the measured values ​​were calculated according to the instrument's operating conditions, and the results are shown in Table 11.

[0217]

[0218] Note: ND means Not Detected.

[0219] The results showed that the recovery rate of the test method of the present invention was between 87.7% and 110.4%, and the RSD was between 0.1% and 7.5%, indicating that the method is very effective.

[0220] Example 5

[0221] Actual sample measurement

[0222] The pretreatment method, instrument operating parameters, and working solution preparation are the same as steps S1 to S5 in Example 4.

[0223] The ten grain samples used in this invention were obtained from physical samples collected during the 2025 food safety supervision and sampling inspection by the Changsha Municipal Institute for Food and Drug Control. These were all commercially available products commonly found in the Changsha market, including rice and wheat flour. The acquisition and use of the samples were approved by the institute and complied with relevant regulations. The inorganic element content in these samples was determined using the aforementioned detection methods, with each sample measured twice in parallel. The results are summarized in Table 12.

[0224]

[0225]

[0226] The results show that this method is applicable to complex and diverse real sample matrices, and has good universality and reliability.

[0227] In summary, the method for determining the content of grains using the super microwave digestion-ICP-OES internal standard method is simple to operate. The accuracy and recovery rate tests are between 87.7% and 110.4%, and the RSD is between 0.1% and 7.5%. The range meets the requirements, and the accuracy and precision meet the expected goals, which can meet the needs of production inspection.

[0228] Example 6

[0229] Method Application

[0230] The established method was applied to the analysis of actual samples. Ninety samples of commercially available rice from 10 major areas of Changsha City were collected as the core research object, and the content of nutrients and heavy metals was systematically determined. Based on this, a dual evaluation was conducted: firstly, the nutrient content was analyzed to assess its nutritional quality; secondly, a health risk assessment model was used to calculate and evaluate the chronic non-carcinogenic and carcinogenic risks of heavy metal intake through diet for adults and children, quantifying its safety for consumption.

[0231] (a) Nutritional Quality Assessment

[0232] (1) Nutritional quality assessment

[0233] Nutritional Quality Assessment (INQ) is an internationally recognized method for dietary evaluation. It assesses the quality of food by evaluating how well its various nutrients meet human needs and provide sufficient calories. This experiment used INQ to evaluate the levels of iron, manganese, copper, zinc, potassium, calcium, sodium, magnesium, and phosphorus in commercially available rice from Changsha. The INQ value was calculated using the following formula:

[0234] .

[0235] In the formula:

[0236] Molecular: The content of a certain nutrient in 100 g of a certain food / the daily requirement of that nutrient for a specified human body;

[0237] Denominator: Calories in 100g of a certain food / Daily calorie requirement of a specified human body.

[0238]

[0239] In this study, the rice samples from Changsha city showed relatively high levels of zinc, manganese, copper, and phosphorus (INQ values ​​> 1), but relatively low levels of potassium, calcium, magnesium, and iron (INQ values ​​< 1). Furthermore, the nutritional index of men was significantly higher than that of women. This indicates that rice is a good source of zinc, manganese, and copper, but not a good source of potassium, calcium, magnesium, and iron. Notably, the abnormally high INQ value for copper suggests a potential risk of excessive intake.

[0240] (2) Mineral nutritional composition analysis

[0241] The calcium-to-magnesium ratio (ω(Ca) / ω(Mg)≈0.38) is far below the ideal dietary range (2-3), indicating that the magnesium content in this rice is significantly higher than its calcium content. Magnesium, as an activator of many enzymes, participates in energy metabolism, nerve conduction, and blood pressure regulation; however, a low calcium-to-magnesium ratio may be associated with a higher risk of hypertension. Therefore, when this rice is a staple food, it is important to supplement calcium through high-calcium foods such as dairy products and soy products to optimize overall mineral balance and support bone health and cardiovascular function.

[0242] The extremely low sodium-potassium ratio (ω(Na) / ω(K)≈0.0031) highlights the typical nutritional advantages of rice as a natural grain, being high in potassium and low in sodium. Potassium plays a crucial role in maintaining intracellular osmotic pressure, acid-base balance, and neuromuscular function, while a high-sodium, low-potassium diet is a significant risk factor for hypertension and cardiovascular disease. Consuming this type of rice can effectively increase dietary potassium intake with almost no increase in sodium load, helping to maintain normal blood pressure and reduce the risk of cardiovascular disease. Therefore, it is suitable for daily consumption by hypertensive patients and healthy individuals.

[0243] The zinc-copper ratio (ω(Zn) / ω(Cu)≈6.22) falls within the commonly recommended dietary range (5-15), indicating a relatively balanced zinc and copper content in this rice. Both zinc and copper are essential trace elements for the human body, jointly participating in antioxidant, immune regulation, and hematopoietic processes. Studies show that the zinc-copper ratio is often imbalanced in patients with hypertension and coronary heart disease, while long-term consumption of foods with a moderate zinc-copper ratio helps maintain or improve the balance of these two elements in the body, potentially having a positive impact on cardiovascular health. This ratio is higher than that of refined foods that may lead to insufficient copper intake, but also far lower than excessively high levels that should be noted, reflecting the stability of rice in terms of trace element supply.

[0244] The manganese-zinc ratio (ω(Mn) / ω(Zn)≈0.55) indicates that manganese is relatively abundant in this rice, which may compete with zinc absorption. This suggests that it is necessary to consciously supplement zinc in daily diet (such as by eating shellfish and red meat) to maintain the balance of the two trace elements in the body and support immune function and cardiovascular health.

[0245] Risk assessment

[0246] This study assessed the non-carcinogenic and carcinogenic risks of ingested heavy metals and other trace elements based on the contaminant detection results of commercially available rice in a certain city. This chapter uses a risk assessment model to quantify the non-carcinogenic and carcinogenic risks. To accurately estimate the potential non-carcinogenic and carcinogenic effects at estimated exposure levels in the population, the assessment of human health risks needs to consider both different doses of metal exposure and the consumption levels of different population groups.

[0247] (3) Risk assessment model

[0248] Exposure Assessment: This study employed the Estimated Daily Intake (EDI) method to assess human exposure to heavy metals through direct consumption of rice. The calculation formula was adapted from the standards of the U.S. Environmental Protection Agency (EPA), and the specific definitions are as follows:

[0249] .

[0250] In the formula: EDI: Daily intake of heavy metals through rice consumption (unit: mg·kg) -1 ・d -1 );

[0251] EF: Exposure Frequency, typically taken as 365 days per year;

[0252] ED: Exposure Duration, typically taken as 70 years;

[0253] C: Concentration of heavy metals in rice (unit: mg / kg dry weight);

[0254] FIR: Food Ingestion Rate, which is the daily rice consumption, measured in kg / d.

[0255] BW: Average weight of consumers, in kg;

[0256] AT: Average Exposure Time, in 365 days. -1 ×70 a.

[0257]

[0258] Data source: Primarily from the "Handbook of Exposure Parameters for the Chinese Population".

[0259] This paper divides the population into adult men, adult women, and children to analyze the exposure of different metal pollutants to the population. The calculation results are shown in Table 15.

[0260]

[0261] (4) Non-carcinogenic risk assessment - target hazard coefficient method

[0262] Since barium (Ba), aluminum (Al), lead (Pb), copper (Cu), manganese (Mn), zinc (Zn), boron (B), molybdenum (Mo), strontium (Sr), and chromium (Cr) are not classified as Group 1 carcinogens, the THQ (Target Hazard Quotients) method established by the U.S. Environmental Protection Agency was primarily used for calculation. Based on the elemental content measured in this experiment, the THQ value of long-term rice consumption was calculated, and the health risk posed by each element was evaluated through the THQ value. The relevant calculation formulas are as follows:

[0263] .

[0264] .

[0265] In the formula:

[0266] THQ: Target Risk Level;

[0267] TTHQ: Total Target Risk Factor;

[0268] RfD: Reference Dose of the element, in mg / kg / d.

[0269] The results show that the impact of lead, mercury, and chromium in rice on the total non-carcinogenic risk (THQ) of different populations is ranked as follows: Mo > Cu > Mn > Zn > Pb > Ni > Cr > B > Ba > Al > Sr. The results are all <1, indicating that these 11 elements do not pose a threat to human health and the risk can be ignored. The total non-carcinogenic risk (TTHQ) assessment shows that the impact on children is greater than that on adult men and adult women.

[0270]

[0271] Data source: Reference to the Integrated Risk Information Database (IRIS) RfD of the US EPA.

[0272] (5) Carcinogenic risk assessment

[0273] The U.S. Environmental Protection Agency (USEPA) defines carcinogenic risk (CR) as "the additional probability that an individual will develop cancer over their lifetime due to exposure to a potential carcinogen." This study only calculated the carcinogenic risk of cadmium and arsenic; the levels of other metals were below the detection limit. The EPA gives the carcinogenic slope factors (CSF) (unit: mg / kg·d) for cadmium (Cd) and arsenic (As) as 0.38 and 1.5, respectively. The carcinogenic risk calculation model for a single heavy metal is as follows:

[0274] ;

[0275] In the formula:

[0276] CR: Lifetime cancer risk from single heavy metal exposure

[0277] SF: Carcinogenic slope factor (mg / kg / d).

[0278] According to the U.S. Environmental Protection Agency's guidelines: if the estimated carcinogenic risk value is less than 10... -6 This can be considered a negligible risk; if it is at 10... -4 ~10 -6 If the risk level is between 10 and 10, it is considered borderline risk; if the carcinogenic risk value exceeds 10... -4 If the risk is too high, it is unacceptable and may have adverse effects, requiring immediate intervention and remedial measures. Different population groups have varying levels of potential carcinogenic risk from arsenic and cadmium exposure due to rice consumption, as shown in Table 17. Among different population groups, the carcinogenic risk value of As was consistently higher than that of Cd when consuming rice. The carcinogenic risk in children is significantly greater than in adult men and women; however, considering that bioavailability was ignored in the calculations, the carcinogenic risk level may be overestimated.

[0279]

[0280] It should be noted that the above embodiments are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is impossible to exhaustively list all possible implementations here. All obvious variations or modifications derived from the technical solutions of this invention are still within the scope of protection of this invention.

Claims

1. A method for determining multiple elements in grains and their products using a super microwave digestion-ICP-OES internal standard method, characterized in that, Includes the following steps: S1: Mix the grain sample with acid and perform super microwave digestion to obtain a digestion solution; S2: Add internal standard solution to digestion solution, make up to volume, and obtain test solution; S3: Prepare elemental standard solutions, wherein the elemental standard solutions contain internal standard elements; S4: A series of mixed elemental standard solutions were determined using inductively coupled plasma atomic emission spectrometry, and standard curves for each element were established. S5: The content of each element in the sample is calculated based on the standard curve using an inductively coupled plasma atomic emission spectrometer to determine the content of the test liquid. The method further includes an internal standard spectral line screening step, which includes: (a) For candidate internal standard spectral lines, obtain the analytical chemical performance index data of each candidate internal standard spectral line in the determination of target elements, wherein the analytical chemical performance index includes at least spike recovery rate, standard deviation of recovery rate, relative error and relative standard deviation; (b) A multi-index decision algorithm is used to comprehensively evaluate the analytical chemical performance index data, and the optimal internal standard spectral line for the target element is selected based on the evaluation results; (c) Based on the selected optimal internal standard spectral lines, an internal standard calibration system for the determination of multiple elements in cereals and their products was constructed; The multi-index decision-making algorithm includes the CRITIC-TOPSIS algorithm; The comprehensive evaluation includes: (b1) The analytical chemical performance data are standardized, wherein the average spiked recovery rate, an intermediate indicator, is standardized with 100% as the ideal value; the standard deviation of recovery rate, relative error and relative standard deviation, three negative indicators, are negatively standardized. (b2) The objective weights of each analytical chemical performance index were calculated using the CRITIC method; (b3) Introduce the objective weights into the TOPSIS method, calculate the relative closeness of each candidate internal standard spectral line, and sort each candidate internal standard spectral line according to the relative closeness; The standardization of average spiked recovery rate is performed using the following formula: （1）； The standardization of negative indicators is performed using the following formula: ； In the formula, X ij The data is after normalization; x j For all original data of the j-th indicator; x ij For the j-th index of the i-th internal standard element; X bestj The ideal value for the j-th index; This represents the maximum absolute value of the difference between the original data of the j-th indicator and the ideal value across all samples. The calculation of the objective weights includes: Calculate the variability of each indicator. ; Calculate the conflict of each indicator ; In the formula, rkj is the Pearson correlation coefficient between the k-th indicator and the j-th indicator. Let be the mean of the j-th indicator across all samples; k Let m be the mean of the k-th indicator across all samples; m be the number of samples; and n be the number of indicators. ； Calculate information content (6); Calculate weights .

2. The method according to claim 1, characterized in that, The grains and their products include at least one of rice, millet, corn, Job's tears, black rice, oats, quinoa, soybeans, wheat flour, and glutinous rice flour.

3. The method according to claim 1, characterized in that, The grains and their products contain multiple elements including one or more of Ca, Mg, K, Na, P, S, Fe, Zn, Al, As, B, Ba, Be, Cd, Co, Cr, Cu, Li, Mn, Mo, Ni, Pb, Rb, Sb, Se, Sn, Sr, Ti, Tl, and V.

4. The method according to claim 1, characterized in that, The super microwave digestion in step S1 adopts a two-step heating program; specifically: the first step is to heat to 100℃~140℃ within 10 min~20 min and hold for 25 min~35 min; the second step is to continue heating to 220℃~280℃ within 5 min~15 min and hold for 15 min~25 min.

5. The method according to claim 1, characterized in that, The internal standard solution in step S2 is a stock solution containing an internal standard element, and the concentration of the internal standard solution is 100-1000 μg / mL; the internal standard solution in step S2 is a solution containing Ge, Re, Y, Bi, In and Rh; preferably, the internal standard element in step S3 is a combination of Ge, Re and Y.

6. The method according to claim 1, characterized in that, The candidate internal standard spectral lines in step (a) include one or more of the following: Bi 190.171 nm, Bi 222.821 nm, Bi 223.061 nm, Ge 206.866 nm, Ge 209.426 nm, Ge 219.871 nm, Ge 265.117 nm, In 230.606 nm, In 325.609 nm, Re 197.248 nm, Re 221.427 nm, Rh 233.477 nm, Rh 343.488 nm, Rh 369.236 nm, Y 324.228 nm, Y 360.074 nm, Y 371.029 nm, and Y 377.433 nm.

7. The method according to claim 1, characterized in that, The calculation of the relative proximity of each candidate internal standard spectral line in step (b3) includes: (b3-1) Constructing a weighted standardized decision matrix: The objective weight W obtained by the CRITIC method j Compared with standardized evaluation data X ij Multiplying these values ​​yields the weighted standardized value zij, forming the weighted standardized decision matrix Z. （8）； ； (b3-2) Determine the positive and negative ideal solutions: The ideal solution Z + The evaluation index is composed of the optimal weighted normalized value among all candidate internal standard spectral lines; that is, for each evaluation index, the maximum value of the weighted normalized value among all candidate internal standard spectral lines is taken; the negative ideal solution Z - It consists of the worst weighted normalized value of each evaluation index among all candidate internal standard spectral lines, that is, for each evaluation index, the minimum weighted normalized value among all candidate internal standard spectral lines is taken; ； ； (b3-3) Calculate the Euclidean distance: Calculate the Euclidean distance D between each candidate internal standard spectral line and the positive ideal solution. i + and the Euclidean distance D to the negative ideal solution. i - The Euclidean distance is calculated by summing the squares of the differences between the weighted standardized values ​​and the ideal values ​​for each evaluation index, and then taking the square root. ； ； (b3-4) Calculate the relative closeness: Based on the distance D between each candidate internal standard spectral line and the positive ideal solution i + The distance D between the sum and the negative ideal solution i - Calculate the relative closeness C i ; （14）； Internal standard spectral line sorting: Based on the calculated relative closeness C i The candidate internal standard spectral lines are sorted in descending order of their values.

8. The method according to claim 1, characterized in that, The optimal internal standard spectral lines in step (c) are as follows: Ge 209.426 nm, Ge 219.871 nm, Ge 265.117 nm, Re 221.427 nm, Y 324.228 nm.

9. The method according to claim 1, characterized in that, The construction of the internal standard calibration system for the determination of multiple elements in cereals and their products in step (c) includes: extracting the top three candidate internal standard spectra in terms of relative proximity of each target element, counting the frequency of each candidate internal standard spectra in the top three among all target elements, determining the common internal standard spectra based on the frequency, and selecting and supplementing the internal standard spectra based on the relative proximity ranking of the target elements for target elements that are not effectively covered by the common internal standard spectra to form a multi-internal standard combination calibration system.

10. A method for screening internal standard spectral lines for the determination of multiple elements in grains and their products, characterized in that, include: (a) For candidate internal standard spectral lines, obtain the analytical chemical performance index data of each candidate internal standard spectral line in the determination of target elements, wherein the analytical chemical performance index includes at least spike recovery rate, standard deviation of recovery rate, relative error and relative standard deviation; (b) A multi-index decision algorithm is used to comprehensively evaluate the analytical chemical performance index data, and the optimal internal standard spectral line for the target element is selected based on the evaluation results; (c) Based on the selected optimal internal standard spectral lines, an internal standard calibration system for the determination of multiple elements in cereals and their products is constructed, wherein the internal standard calibration system consists of internal standard spectral lines of Ge 209.426 nm, Ge 219.871 nm, Ge 265.117 nm, Re 221.427 nm, and Y 324.228 nm.

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