A method for quality prediction of tinplate cans based on iron ion migration amount

Abstract

The application discloses a method for quality prediction of tinplate cans based on iron ion migration amount, which comprises the following steps: sealing and soaking the tinplate cans filled with acetic acid, and performing temperature acceleration test; sampling on the third day of the temperature acceleration test, and detecting the iron ion migration amount; sampling on the tenth day of the temperature acceleration test, and detecting the iron ion migration amount; and predicting the quality of the tinplate cans based on the iron ion migration amounts on the third day and the tenth day. The application takes the migration amount on the third day as a short-term risk indicator, and takes the migration amount on the tenth day as a key quality determination standard of long-term stability indicator. Based on the detection results of the two time points, the iron ion migration trend of the tinplate cans within a six-month shelf period can be reliably predicted, the sensory quality and stability of beer products can be ensured, clear and quantitative delivery access conditions of suppliers can be set according to the detection results, the risk of beer quality deterioration caused by quality problems of packaging materials can be avoided from the source, early prediction and precise control of the quality of packaging materials are realized, and the overall quality management level is improved.

Description

Technical Field

[0001] This invention belongs to the field of beer packaging and quality testing technology, specifically relating to a method for quality prediction of tin cans based on iron ion migration. Background Technology

[0002] In beer production, tin cans are a common packaging material, and the iron ions that may leach from them have a significant impact on the sensory quality and shelf-life stability of the product. Therefore, accurate detection of iron ion content in beer has become a key aspect of quality control. Currently, the o-phenanthroline spectrophotometric method is commonly used to determine the iron ion concentration in beer. This method is widely used as an important basis for evaluating beer quality, predicting flavor stability, and judging shelf-life performance.

[0003] However, existing technologies for detecting iron ions in tin-canned beer have a detection lag. Iron ion detection in finished beer is the final stage of production and cannot identify or control the risks associated with iron ions introduced through the tin can. This could lead to a metallic taste in the finished beer due to high iron ion levels in the tin can, resulting in the spoilage of packaged products and causing direct economic losses and resource waste.

[0004] Therefore, there is an urgent need for a method that can accurately predict the amount of iron ion migration in tin cans in order to achieve quality control of beer packaging materials and thus control the quality of beer production. Summary of the Invention

[0005] The purpose of this invention is to address the above-mentioned technical problems by providing a method for accurately and effectively predicting the amount of iron ion migration in tin cans.

[0006] To achieve the above objectives, the present invention provides a method for predicting the quality of tin cans based on iron ion migration, comprising the following steps: S1. Seal and immerse a tin can filled with acetic acid in it, and conduct an accelerated heating test. S2. Samples were taken on the third day of the accelerated heating test to detect the amount of iron ion migration; S3. Samples were taken on the 10th day of the accelerated heating test to detect the amount of iron ion migration; S4. Predict the quality of tin cans based on the amount of iron ion migration on day 3 and day 10.

[0007] As a preferred embodiment of the present invention, the intended use conditions for the tin can are storage at room temperature for more than 30 days to 180 days or less.

[0008] In a preferred embodiment of the present invention, the volume concentration of acetic acid in step S1 is 4%.

[0009] In a preferred embodiment of the present invention, the accelerated heating test conditions in step S1 are 50°C for 10 days.

[0010] In a preferred embodiment of the present invention, in step S1, the accelerated heating test is carried out in a 50°C constant temperature chamber.

[0011] In a preferred embodiment of the present invention, in step S4, when the iron ion migration amount is ≤0.2mg / L on day 3 and ≤5mg / L on day 10, the quality is deemed to be qualified. If the iron ion migration result on day 3 is >0.2 mg / L, then the quality is considered qualified if the result on day 10 is ≤5 mg / L. The quality is deemed unqualified when the iron ion migration result on day 3 is >0.2 mg / L and on day 10 is >5 mg / L.

[0012] On the other hand, the present invention provides a method for predicting the quality of beer based on the migration of iron ions in tin cans, which includes the following steps: S21. Fill tin cans used for beer packaging with acetic acid, seal and immerse them, and conduct an accelerated heating test. S22. Samples were taken on the third day of the accelerated heating test to detect the amount of iron ion migration; S23. Samples were taken on the 10th day of the accelerated heating test to detect the amount of iron ion migration; S24. Based on the iron ion migration on day 3 and day 10, predict the quality of the beer to be packaged.

[0013] In a preferred embodiment of the present invention, the volume concentration of acetic acid in step S21 is 4%.

[0014] In a preferred embodiment of the present invention, in step S21, the accelerated heating test conditions are 50°C for 10 days.

[0015] In a preferred embodiment of the present invention, in step S21, the accelerated heating test is carried out in a 50°C constant temperature chamber.

[0016] In a preferred embodiment of the present invention, in step S24, when the iron ion migration amount is ≤0.2mg / L on the 3rd day and ≤5mg / L on the 10th day, the beer quality is predicted to be qualified. If the iron ion migration result on day 3 is >0.2 mg / L, then the beer quality is predicted to be qualified when the result on day 10 is ≤5 mg / L. When the iron ion migration result on day 3 is >0.2 mg / L and the result on day 10 is >5 mg / L, the beer quality is predicted to be substandard.

[0017] In a preferred embodiment of the present invention, in step S24, the iron content of beer in a tin can is predicted by a linear mixed-effects model.

[0018] As a preferred embodiment of the present invention, the prediction formula is as follows: Iron ion content = -0.1014 + 0.02627 × month.

[0019] This invention establishes a highly efficient and sensitive quantitative analysis method for iron ion migration in tin cans, enabling early prediction and precise control of packaging material quality. This method quantitatively analyzes the migration of iron ions in tin cans, establishing a highly efficient and sensitive detection method, and its reliability has been verified through systematic experiments. Due to quality differences between different batches of tin cans, the migration results of iron ions on the 3rd and 10th days after soaking cans can be used to effectively predict the iron ion migration in finished beer from tin cans over a shelf life (6 months) and the impact of iron ions on beer flavor. The core of this invention lies in establishing key quality judgment criteria through systematic experimental verification, using "migration on day 3" as a short-term risk indicator and "migration on day 10" as a long-term stability indicator. Based on these dual-time-point test results, we can not only reliably predict the iron ion migration trend of tin cans during the 6-month shelf life, ensuring the sensory quality and stability of the finished beer, but also set clear and quantifiable delivery access conditions for suppliers. This ensures the quality of incoming tin cans and effectively intercepts high-risk packaging materials before they are put into production, transforming passive quality control into proactive prevention. This avoids the risk of beer quality deterioration caused by packaging material quality problems from the source and improves the overall quality management level. Attached Figure Description

[0020] Figure 1 It is an absorbance-concentration standard curve.

[0021] Figure 2 The study shows the trend of iron ion content in tin cans of beer of different quality groups over time.

[0022] Figure 3 The distribution of sensory evaluations over time is shown.

[0023] Figure 4 The relationship between short-term migration and long-term content is shown.

[0024] Figure 5 A linear mixed-effects model is shown.

[0025] Figure 6 The multinomial regression model is shown.

[0026] Figure 7 The predictive performance of the linear mixed-effects model and the multinomial regression model is shown. Detailed Implementation

[0027] To facilitate understanding of the present invention, a more complete description will be given below with reference to specific embodiments. Preferred embodiments of the invention are shown in the accompanying drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of the invention.

[0028] In the description of this invention, unless otherwise explicitly defined, terms such as heating, cleaning, weighing, and freezing should be interpreted broadly. Those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0029] In the description of this invention, references to terms such as "some embodiments" and "examples" indicate that the specific methods or materials described in connection with that embodiment or example are included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the specific methods and materials described may be combined in any suitable manner in one or more embodiments or examples.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0031] Unless otherwise specified, the experimental methods used in the following examples and comparative examples are conventional methods, and the materials and reagents used are commercially available unless otherwise specified.

[0032] Example 1: Detection of iron ion migration in tin cans According to GB31604.1-2023 "National Food Safety Standard General Rules for Migration Test of Food Contact Materials and Articles" 4.1 General requirements for the selection of food simulants, beer is an acidic food with pH < 5. According to the guidelines, 4% (volume fraction) acetic acid is selected as the food simulant for migration test.

[0033] Specific migration accelerated testing conditions: When the expected use conditions are room temperature storage for more than 30 days to less than 180 days (including hot filling pasteurization or other heat treatment), the accelerated testing conditions are 50°C for 10 days.

[0034] The following technical solution is adopted: 1. Main experimental equipment Spectrophotometer, analytical balance, incubator, water bath, pipette, pipette, volumetric flask, stoppered colorimetric tube.

[0035] 2. Experimental Materials All reagents used were of analytical grade, and the solutions were prepared with laboratory grade III water.

[0036] 2.1 Colorimetric reagent: Weigh 0.3g of o-phenanthroline, add two drops of concentrated hydrochloric acid to aid dissolution, and add water to make up to 100mL.

[0037] 2.2 Iron standard stock solution (100 μg / mL): Prepared according to GB / T 602-2002.

[0038] 2.3 Iron standard working solution (10 μg / mL): Pipette 10.0 mL of stock solution and dilute to 100 mL.

[0039] 2.4 Ascorbic acid 3. Sample pretreatment Sample pretreatment: Seal and immerse a tin can filled with 4% acetic acid in a 50°C incubator. Take samples on day 3 and day 10 to detect iron ion migration. The immersion solution should be clear, odorless, and free of precipitates.

[0040] 4. Testing Procedures 4.1 Take 25.0 mL of the same sample and place it in two colorimetric tubes, A and B.

[0041] 4.2 Add 25 mg of ascorbic acid and 2 mL of colorimetric reagent to tube A; add 25 mg of ascorbic acid and 2 mL of water to tube B. Place both tubes in a water bath at 60±0.5℃ for 15 min. Cool to room temperature. Measure the absorbance of tube A at 505 nm using tube B as a reference, and calculate the iron content using a standard curve.

[0042] 4.3 Precautions The samples taken on day 3 and day 10 were taken from the same container.

[0043] Example 2 1. Plotting the standard curve Using a pipette, pipette 0.00 mL, 1.00 mL, 2.50 mL, 5.00 mL, 10.00 mL, 15.00 mL, 20.00 mL, 25.00 mL, 30.00 mL, and 35.00 mL of the iron standard working solution into ten 50 mL volumetric flasks, respectively. Dilute to volume with water to obtain iron standard solutions of 0.00 mg / L, 0.20 mg / L, 0.50 mg / L, 1.00 mg / L, 2.00 mg / L, 3.00 mg / L, 4.00 mg / L, 5.00 mg / L, 6.00 mg / L, and 7.00 mg / L (the concentration of the sample should not exceed the concentration range of the standard curve; otherwise, it needs to be reset according to the actual situation). Pipette 25 mL of each of the prepared iron standard solutions into ten 50 mL stoppered colorimetric tubes. Add 25 mg of ascorbic acid and 2 mL of colorimetric reagent, mix thoroughly, and place in a water bath at (60 ± 0.5) °C for 15 min. Remove and rapidly cool to room temperature. Measure the absorbance at a wavelength of 505 nm, using the zero point of the standard series working solutions as a reference. Plot a standard curve with absorbance values ​​on the x-axis and the concentration of the standard series working solutions on the y-axis (see Table 1). Figure 1 .

[0044] Table 1. Calibration Curves

[0045] From Table 1, Figure 1 It can be seen that a standard curve for iron ion concentrations in the range of 0.2–7.0 mg / L was constructed using the o-phenanthroline spectrophotometric method, and the correlation coefficient (R) was [value missing]. 2 The value was 0.9999, which is greater than 0.999. This indicates that the method has good linearity and can meet the requirement for accurate quantification of trace iron ions in the migration liquid of tin cans.

[0046] 2. Repeatability test The repeatability and accuracy of the method were verified by performing six repeat tests on a 0.20 mg / L iron ion standard solution.

[0047] The detailed results are shown in Table 2.

[0048] Table 2. Repeatability Validation Data

[0049] As shown in Table 2, under the same experimental personnel, instruments, and operating procedures, the iron ion migration of the same standard solution sample was repeatedly measured six times. The average deviation between the measured and theoretical values ​​was 8.5%, which meets the standard requirement (≤10%). The repeatability RSD was 2.29%, also meeting the standard requirement (≤10%). Accuracy and repeatability are good. The results demonstrate high repeatability, verifying the stability of the pretreatment steps and colorimetric reaction, and providing data support for the reliability of routine testing.

[0050] 3. Reproducibility test Three inspectors, A, B, and C, used two different models of spectrophotometers to perform reproducibility tests on the same sample that had been soaked for 10 days. The results are detailed in Table 3.

[0051] Table 3. Reproducibility Validation Data

[0052] As shown in Table 3, repeated testing under different personnel and instrument conditions (differences in spectrophotometer models) yielded a reproducibility coefficient of variation (RSD) of 0.78% for the same sample, meeting the standard requirement (≤40%), indicating good repeatability. The results of iron ion migration determination demonstrate that this method has low sensitivity to operational details and instrument differences, possessing the potential for cross-laboratory application and supporting the standardized promotion of tin can quality and safety testing.

[0053] 4. Determination of recovery rate Take food simulant samples (4% acetic acid) soaked for 10 days at known concentrations, and add 0.20 mg / L, 1.00 mg / L, and 2.00 mg / L iron ion standard working solutions respectively. The measured values ​​and recovery rate calculation results are detailed in Table 4.

[0054] Table 4. Determination of spiked recovery

[0055] As shown in Table 4, the recovery rates were 97.5%, 98.1%, and 98.7% respectively (the standard requirement is 80-110%), all of which meet the standard requirements. The high recovery rate confirms the anti-interference ability of the o-phenanthroline colorimetric system against coexisting components (such as organic acids and salt ions) in the food simulation liquid, and its accuracy meets the requirements of the food contact material testing standards.

[0056] Example 3 Eighteen batches of tin cans were taken, with three cans made from each batch. The iron ion content of the soaking solution was tested on the 3rd and 10th days according to the above testing method. The results are detailed in Table 5 below.

[0057] Table 5. Iron ion content in the soaking solution

[0058] The iron ion content (tested according to the method in GB / T4928-2008.C.7.1) and the organizational evaluation of the above 18 batches of beer produced in tin cans were tracked and tested. The results are detailed in Table 6.

[0059] Table 6. Iron ion content and evaluation results

[0060] The following conclusions can be drawn from Tables 5 and 6: (1) When the migration of iron ions on the 3rd and 10th days of batches 1 to 6 was less than 0.20 mg / L and less than 5.00 mg / L respectively, the iron ion content of the beer within 6 months did not exceed 0.05 mg / L, and the evaluation results were all normal.

[0061] (2) When the migration of iron ions in tin cans of batches 7-12 was greater than 0.20 mg / L and less than 5.00 mg / L on the 3rd and 10th days, respectively, professional tasters could identify a slight metallic taste when the iron content of the beer produced was between 0.05 and 0.10 mg / L, but it did not significantly interfere with ordinary consumers.

[0062] (3) When the migration of iron ions in tin cans of batches 13-18 is greater than 0.20 mg / L on the 3rd day and greater than 5.00 mg / L on the 10th day, the iron ion content of the corresponding finished beer is between 0.10 and 0.30 mg / L. Ordinary consumers and professional tasters can perceive a distinct metallic taste, which is a quality defect. When the iron ion content is greater than 0.30 mg / L, there is a strong metallic taste, which is determined to be unacceptable to consumers.

[0063] 5. Forecast of Iron Ion Content Trend in Tin Can Beer 5.1 Data Structure (Tables 7 and 8) Table 7

[0064] Table 8 5.2 Statistical Summary To simulate the differences in performance of tin cans from different batches or made of different materials during storage, the test samples were divided into three quality grade groups (Groups A, B, and C), as shown in Table 9. By comparing the changes in different groups, a more accurate predictive model was established.

[0065] Group A (Superior): Low initial iron ion content (0.006 - 0.045 mg / L).

[0066] Group B (Medium): Initial concentration was moderate (0.028 - 0.095 mg / L).

[0067] Group C (inferior quality): High initial content (0.073 - 0.558 mg / L).

[0068] Table 9

[0069] Figure 2 The study shows the trend of iron ion content in tin cans of beer of different quality groups over time.

[0070] Figure 3 The distribution of sensory evaluations over time is shown.

[0071] Figure 4 The relationship between short-term migration and long-term content is shown.

[0072] 5.2 Establishing a Predictive Model 5.2.1 Linear Mixed Effects Model Considering the effect of time on iron ion content (linear part) and the inherent differences between different groups (A / B / C) (mixed effect part), assuming that the trends of all cans are roughly the same, but allowing each group to have its own "baseline" and "growth rate", a linear mixed effect model is constructed. Figure 5 ).

[0073] (1) Basic information of the model Fitting method: The linear mixture model uses restricted maximum likelihood (REML) to estimate the parameters. This method can estimate the variance component more accurately when random effects are included.

[0074] Hypothesis testing method: The significance test of fixed effects is performed using the Satterthwaite approximation method (implemented by the lmerTest package), which solves the problem of inaccurate degree of freedom estimation in traditional LMM.

[0075] Model formula: Fe_content ~ month + Fe_3d_mean + Fe_10d_mean + (1 | group) The response variable is Fe_content (iron content), and the fixed effects are month, Fe_3d_mean (mean iron content over 3 days), and Fe_10d_mean (mean iron content over 10 days). The random effect is (1 | group), which means that the intercept of "group" is random, that is, the baseline iron content of different groups is different, but the slope is fixed.

[0076] (2) Model fit and residuals The convergence value of the REML criterion is -267.9. The smaller the value, the better the model fits the data.

[0077] Standardized residuals: Residuals are the differences between observed values ​​and model predictions. Standardization makes it easier to determine whether the distribution is reasonable. Min=-2.32831, Max=2.37708: The residuals range from -3 to 3, with no obvious extreme outliers; Median=0.02969 is close to 0, indicating that the overall distribution of residuals is relatively symmetrical.

[0078] (3) Random Effects The grouping variable is "group" (18 groups in total, see "Sample Size" below); the random effect is the "intercept," which indicates the difference in baseline iron content between groups; the variance of the between-group intercept is 0.001972, and the standard deviation is 0.04441; the variance of the residuals (within-group variation) is 0.003045, and the standard deviation is 0.05518. The variation in the between-group intercept (0.001972) is smaller than the variation in the within-group residuals (0.003045), indicating that the "group" has limited explanatory power for iron content, but there is indeed some difference between groups.

[0079] (4) Fixed Effects Fixed effects describe the "average effect of the independent variable on the response variable", including indicators such as estimate, standard error, degrees of freedom (df), t-value, and p-value (Pr(>|t|)).

[0080] This is a linear model that takes into account the differences between different batches of tanks, and it was found that time is the only significant influencing factor.

[0081] Between-group differences were taken into account: 18 different combinations of tanks were used as random factors.

[0082] Linear relationship: It is assumed that the iron ion content increases linearly with time.

[0083] Prediction formula: Iron ion content = -0.1014 + 0.02627 × month.

[0084] Key findings included the following influencing factors: Month: Extremely significant (p<0.001), with an increase of 0.02627 mg / L in iron ions for each additional month; Day 3 test: Not significant (p = 0.662); Day 10 test: Not significant (p = 0.639).

[0085] The model gives a performance metric of RMSE (root mean square error) of 0.0509, which is relatively low, indicating that the model can fit the actual data well.

[0086] 5.2.2 Polynomial Regression Model (Considering Nonlinear Trends) Assuming that the iron ion content does not increase linearly with time, but rather increases in a curve (such as a parabola), a multinomial regression model is constructed. Figure 6 ).

[0087] This is a model that attempts to capture the trend of the curve, but the quadratic term is not significant, indicating that the release of iron ions is basically linear.

[0088] The trend of the curve was taken into account: the square of the month (month_sq) was added.

[0089] The linear relationship is dominant: the quadratic term is not significant, indicating that the curve characteristics are not obvious.

[0090] High fit: R 2 = 0.8004, which explains 80% of the data variation.

[0091] Key findings include: Overall model: highly significant (p < 2.2e-16); Monthly: marginally significant (p = 0.078); Monthly squares: not significant (p = 0.679), indicating no clear trend; Short-term test: still not significant.

[0092] This model gives a performance metric of RMSE of 0.0676, which is larger than that of the linear mixed-effects model.

[0093] 5.2.3 Comparing Model Performance Comparing the two models above, the linear mixed effects model is better because: (1) it better matches the data characteristics and takes into account the differences between different batches of tanks; (2) it is simpler and does not require complex curve terms; (3) the prediction is more stable and the linear relationship is simple and reliable; (4) the interpretation is more intuitive and easier to understand.

[0094] 5.4 Visualized Prediction Results Figure 7 The prediction performance of the two models is shown in the figure. As can be seen from the figure, the data points of the mixed-effects model are generally closer to the red diagonal, indicating that the deviation between the predicted and actual iron ion content is smaller, and the residual distribution is more concentrated. The data points of the multinomial model are relatively more away from the red diagonal, especially in the low and high concentration regions, where the deviation between the predicted and actual values ​​is more obvious, and the residual distribution is more dispersed. It is clear that the mixed-effects model has a better prediction performance.

[0095] According to the method of this invention, the migration of iron ions in the finished beer made in tin cans during the shelf life (6 months) and the impact of iron ions on the beer flavor can be effectively predicted based on the migration results of iron ions on the 3rd and 10th days after soaking in the tin cans. Delivery conditions for the cans are set for the supplier based on the iron ion migration results to ensure the quality of the incoming tin cans. When the iron ion migration result after 3 days is ≤0.2 mg / L and after 10 days is ≤5 mg / L, the quality is considered qualified and can be used normally in production; when the iron ion migration result after 3 days is >0.2 mg / L, the quality must wait until the result after 10 days is ≤5 mg / L before delivery is considered qualified; when the iron ion migration result after 3 days is >0.2 mg / L and after 10 days is >5 mg / L, the quality is considered unqualified and delivery is not allowed.

[0096] This invention establishes a highly efficient and sensitive quantitative analysis method for iron ion migration in tin cans, enabling early prediction and precise control of packaging material quality. Through systematic experimental verification, key quality judgment criteria were established, using "migration on day 3" as a short-term risk indicator and "migration on day 10" as a long-term stability indicator. Based on these dual-time-point detection results, not only can the iron ion migration trend of tin cans be reliably predicted over a 6-month shelf life, ensuring the sensory quality and stability of finished beer, but it also allows for setting clear and quantifiable delivery access conditions for suppliers. This effectively intercepts high-risk packaging materials before they are put into production, transforming passive quality control into proactive prevention. This avoids the risk of beer quality deterioration caused by packaging material quality problems at the source, improving overall quality management.

[0097] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for predicting the quality of tin cans based on iron ion migration, characterized in that, It includes the following steps: S1. Seal and soak a tinplate can filled with acetic acid, and conduct a temperature-accelerated test; S2. Take a sample on the 3rd day of the temperature-accelerated test and detect the iron ion migration amount; S3. Take a sample on the 10th day of the temperature-accelerated test and detect the iron ion migration amount; S4. Predict the quality of the tinplate can based on the iron ion migration amounts on the 3rd day and the 10th day.

2. The method according to claim 1, characterized in that, The expected use condition of the tinplate can is storage at room temperature for more than 30 days up to and including 180 days.

3. The method according to claim 1, characterized in that, In the step S1, the volume concentration of acetic acid is 4%.

4. The method according to claim 1, characterized in that, In the step S1, the temperature-accelerated test condition is 50 °C for 10 days.

5. The method according to claim 1, characterized in that, In the step S1, the temperature-accelerated test is carried out in a constant temperature oven at 50 °C.

6. The method according to claim 1, characterized in that, In the step S4, when the result of the iron ion migration amount on the 3rd day ≤ 0.2 mg / L and the result on the 10th day ≤ 5 mg / L, it is determined that the quality is qualified; When the result of the iron ion migration amount on the 3rd day > 0.2 mg / L, it is necessary to wait until the result on the 10th day ≤ 5 mg / L to determine that the quality is qualified; When the result of the iron ion migration amount on the 3rd day > 0.2 mg / L and the result on the 10th day > 5 mg / L, it is determined that the quality is unqualified.

7. A method for predicting beer quality based on the migration of iron ions in tin cans, characterized in that, It includes the following steps: S21. Seal and soak a tinplate can used for beer packaging filled with acetic acid, and conduct a temperature-accelerated test; [[ID=1�]]S22. Take a sample on the 3rd day of the temperature-accelerated test and detect the iron ion migration amount; S23. Take a sample on the 10th day of the temperature-accelerated test and detect the iron ion migration amount; S24. Predict the quality of the beer to be packaged based on the iron ion migration amounts on the 3rd day and the 10th day.

8. The method according to claim 7, characterized in that, In the step S1, the volume concentration of acetic acid is 4%.

9. The method according to claim 7, characterized in that, In the step S1, the temperature-accelerated test condition is 50 °C for 10 days.

10. The method according to claim 7, characterized in that, In the step S24, when the result of the iron ion migration amount on the 3rd day ≤ 0.2 mg / L and the result on the 10th day ≤ 5 mg / L, it is predicted that the beer quality is qualified; When the result of the iron ion migration amount on the 3rd day > 0.2 mg / L, it is necessary to wait until the result on the 10th day ≤ 5 mg / L to predict that the beer quality is qualified; When the result of the iron ion migration amount on the 3rd day > 0.2 mg / L and the result on the 10th day > 5 mg / L, it is predicted that the beer quality is unqualified.

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