A data processing method for alpha and beta discrimination measurement of a liquid scintillation counter

Abstract

The application belongs to the technical field of radioactivity measurement, and relates to a data processing method for alpha and beta discrimination measurement of a liquid scintillation counter. α and tau β and calculates total error discrimination rate; step S2, confirming optimal discrimination parameters by using total error discrimination rate; step S3, evaluating alpha / beta discrimination effect by using minimum value of total error discrimination rate and half-peak width of the curve of total error discrimination rate and discrimination parameters; step S4, obtaining a scintillation signal pulse length distribution diagram, and using displacement and slope change of tau α curve and tau β curve to indicate change of scintillation signal pulse length and its probability distribution; and step S5, using trend change of the probability distribution diagram of scintillation signal pulse length to identify alpha signals of a sample. The application realizes comparison of alpha / beta discrimination measurement data under different conditions and comparison of change of scintillation signal pulse length and its probability distribution, and is helpful to optimization of alpha / beta discrimination measurement technology and improvement of reliability of measurement results.

Description

Technical Field

[0001] This invention belongs to the field of radiometric measurement technology, specifically relating to a data processing method for α and β discrimination measurements using a liquid scintillator. Background Technology

[0002] Liquid scintillation analyzer α / β discrimination measurement utilizes the liquid scintillation analyzer to distinguish the different scintillation signals generated by α and β particles in the scintillation fluid, thereby achieving simultaneous measurement of α and β radioactivity. The decay time of the pulse signal generated by α particles is significantly longer than that of the scintillation signal from β particles or gamma rays. Based on the difference in the shape of the scintillation signal pulse, α and β / γ radioactivity can be effectively distinguished. In the α / β discrimination measurement module of the liquid scintillation analyzer, specialized discrimination parameters are used to indicate the shape of the pulse signal. For example, the Quantumulus 1220 liquid scintillation analyzer uses PSA (value range 1-256), and the Hidex 300SL liquid scintillation analyzer uses PLI limit (value range 0-31). By comparing the measured value of the scintillation signal discrimination parameter with the set value, the scintillation signal is classified (less than the set value is classified as β, and greater than or equal to the set value is classified as α).

[0003] The α / β false discrimination rate directly reflects the α / β discrimination effectiveness—the lower the α / β false discrimination rate, the better the α / β discrimination effectiveness. The α / β false discrimination rate includes τ. α and τ β , representing the proportion of α signals miscounted as β signals and the proportion of β signals miscounted as α signals, respectively, are calculated using the following formulas:

[0004]

[0005] in,

[0006] r sα,β The count rate of the pure α-nucleoside sample within the β measurement window;

[0007] r 0,β The count rate of the blank sample within the β measurement window;

[0008] r sα,T The total count rate of the pure α-nuclein sample in the α and β measurement windows;

[0009] r s0,T The total count rate of the blank sample in the α and β measurement windows.

[0010]

[0011] in,

[0012] r sβ,α The count rate of a pure β-nucleoside sample within the α measurement window;

[0013] rs0,α The count rate of the blank sample within the β measurement window;

[0014] r sβ,T The total count rate of the pure β-nuclein sample in the α and β measurement windows;

[0015] r s0,T The total count rate of the blank sample in the α and β measurement windows.

[0016] The α / β false discrimination rate is closely related to the setting of the discrimination parameters. When performing α / β discrimination measurements using a liquid scintillation analyzer, it is first necessary to obtain the optimal discrimination parameters for a specific sample. Currently, there are two methods for obtaining the optimal discrimination parameters: the cross-curve method and the second derivative method. The cross-curve method involves measuring α and β nuclide-spiked samples prepared under the same conditions as the sample to be tested under different discrimination parameter conditions to obtain the τ. α and τ β The relationship curve between the discrimination parameter (denoted as τ) α Curve and τ β (Curve). Generally, τ α It increases with the increase of the discrimination parameter, τ β It decreases as the discrimination parameter increases, τ α Curve and τ β The discrimination parameter corresponding to the intersection point of the curves is the optimal discrimination parameter (i.e., τ). α and τ β (Simultaneously reduced to the minimum value). The second derivative method involves repeatedly measuring the spiked sample or the sample to be tested using different discrimination parameters to obtain a curve showing the relationship between the count rate and the discrimination parameter. The discrimination parameter at the inflection point of the curve is then taken as the optimal value. Since the discrimination parameter is a discrete value, the crossover point obtained by the cross curve method or the inflection point obtained by the second derivative method often lies between two adjacent discrete values. In this case, it is not possible to follow the "τ" rule. α and τ β The optimal discrimination parameter is determined based on the principle of "simultaneously reducing to the minimum value". Furthermore, neither of the above two methods can directly compare the effects of α / β discrimination under different conditions (instrument parameters or sample preparation conditions). Summary of the Invention

[0017] The purpose of this invention is to provide a simple, intuitive, and reliable data processing method to address the data analysis needs of determining the optimal discrimination parameters and comparing the α / β discrimination effects under different conditions (instrument parameters or sample preparation conditions) during the α / β discrimination measurement process of a liquid scintillation analyzer.

[0018] To achieve the above objectives, the present invention employs a data processing method for α and β discrimination measurements using a liquid scintillation meter, comprising the following steps:

[0019] Step S1: Obtain τ under different discrimination parameter conditions.α and τ β And calculate the total false positive rate;

[0020] Step S2: Determine the optimal discrimination parameters using the total false discrimination rate;

[0021] Step S3: Evaluate the α / β discrimination effect using the minimum value of the total false discrimination rate and the half-width of the curve relating the total false discrimination rate to the discrimination parameter;

[0022] Step S4: Obtain the flicker signal pulse length distribution map, using τ α Curve and τ β The shift and slope of the curve indicate changes in the length of the scintillation signal pulse and its probability distribution;

[0023] Step S5: Identify the α signal of the sample by analyzing the trend change of the probability distribution map of the scintillation signal pulse length.

[0024] Furthermore, in step S1, the formula for calculating the total false positive rate is:

[0025] τ=τ α +τ β Formula (3)

[0026] in,

[0027] τ represents the total false discrimination rate, expressed in %.

[0028] τ α The percentage of α signals that are mistakenly counted as β signals, expressed in %.

[0029] τ β The percentage of β signals that are mistakenly counted as α signals, expressed as a percentage.

[0030] Furthermore, in step S2, the optimal discrimination parameter corresponds to the minimum value of the total false discrimination rate.

[0031] Furthermore, in step S3, the relationship curve between the total false discrimination rate and the discrimination parameter is denoted as the τ curve, with the discrimination parameter as the horizontal axis and the total false discrimination rate as the vertical axis.

[0032] Furthermore, in step S3, the half-width of the curve relating the total false positive rate to the discrimination parameters is the distance between the two discrimination parameters where the total false positive rate is equal to 50%.

[0033] Furthermore, in step S4, the slope is τ α and τ β The slope of the linearly fitted curve for all data points in the range of 15%-85%.

[0034] Furthermore, in step S4, the flicker signal pulse length distribution map uses the discrimination parameter as the horizontal axis and the count, count rate, or normalization ratio as the vertical axis.

[0035] Furthermore, in step S5, the trend change of the flash signal pulse length distribution map is the concavity and convexity of the curve.

[0036] Furthermore, in step S5, the α signal of the sample is the count of the α signal recorded after the sample has been measured by the liquid scintillation meter α / β.

[0037] The beneficial effects of this invention are as follows:

[0038] This invention addresses the data analysis needs of α / β discrimination measurements using liquid scintillation analyzers, providing a simple, intuitive, and reliable method for processing α / β discrimination measurement data. The invention uses the total false discrimination rate as the criterion to ensure the uniqueness of the optimal discrimination parameters; it establishes a quantitative comparison method for α / β discrimination effects, enabling the comparison of α / β discrimination measurement data under different conditions (instrument measurement conditions or sample preparation conditions); and it establishes a method utilizing the false discrimination rate curve (τ). α Curve and τ β A method for obtaining the pulse length and probability distribution information of scintillation signals (curve) was developed, enabling the comparison of changes in the pulse length and probability distribution of scintillation signals under different conditions (instrument measurement conditions or sample preparation conditions). A sample scintillation signal identification method was established using the pulse length distribution law of α- and β-nuclides, which helps to improve the reliability of α / β discrimination measurement results. Attached Figure Description

[0039] Figure 1 This refers to the τ curve in Embodiment 1 of the specific implementation section of this invention;

[0040] Figure 2 These are the τ curves of different scintillation fluids in Example 2 of the specific embodiments of this invention;

[0041] Figure 3 This refers to τ under different sample preparation conditions in Example 3 of the specific embodiments of the present invention. α curve;

[0042] Figure 4 This refers to τ under different sample preparation conditions in Example 3 of the specific embodiments of the present invention. β curve;

[0043] Figure 5 This is a pulse length distribution diagram of the scintillation signals of pure α, pure β and blank samples in Example 4 of the specific embodiments of the present invention;

[0044] Figure 6This is a pulse length distribution diagram of the scintillation signal of the α and β mixed spiked sample in Example 4 of the specific embodiments of the present invention. Detailed Implementation

[0045] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0046] The present invention provides a data processing method for α and β discrimination measurements using a liquid scintillation meter, comprising the following steps:

[0047] Step S1: Obtain τ under different discrimination parameter conditions. α and τ β And calculate the total false positive rate;

[0048] Step S2: Determine the optimal discrimination parameters using the total false discrimination rate;

[0049] Step S3: Evaluate the α / β discrimination effect using the minimum total false discrimination rate and the half-width of the curve showing the relationship between the total false discrimination rate and the discrimination parameter;

[0050] Step S4: Obtain the flicker signal pulse length distribution map, using τ α Curve and τ β The shift and slope of the curve indicate changes in the length of the scintillation signal pulse and its probability distribution;

[0051] Step S5: Identify the α signal of the sample by analyzing the trend change of the probability distribution graph of the scintillation signal pulse length.

[0052] In step S1, the formula for calculating the total false positive rate is:

[0053] τ=τ α +τ β Formula (3)

[0054] in,

[0055] τ represents the overall false positive rate, expressed in %.

[0056] τ α The percentage of α signals that are mistakenly counted as β signals, expressed in %.

[0057] τ β The percentage of β signals that are mistakenly counted as α signals, expressed as a percentage.

[0058] In step S2, the optimal discrimination parameter corresponds to the minimum total false discrimination rate.

[0059] In step S3, the relationship curve between the total false discrimination rate and the discrimination parameter is denoted as the τ curve, with the discrimination parameter as the horizontal axis and the total false discrimination rate as the vertical axis.

[0060] In step S3, the half-width of the curve relating the total false positive rate to the discrimination parameters is the distance between the two discrimination parameters where the total false positive rate is equal to 50%.

[0061] In step S4, the slope is τ α and τ β The slope of the linearly fitted curve for all data points in the range of 15%-85%.

[0062] In step S4, the flicker signal pulse length distribution diagram uses the discrimination parameter as the horizontal axis and the count, count rate, or normalization ratio as the vertical axis.

[0063] In step S5, the trend change of the flash signal pulse length distribution map is the concavity and convexity of the curve.

[0064] In step S5, the α signal of the sample is the count of the α signal recorded after the sample has been measured by the liquid scintillation meter α / β.

[0065] Example 1: Determination of Optimal Discrimination Parameters

[0066] Principle: A set of pure α and pure β nuclide spiked samples and a blank sample were prepared under the same sample preparation conditions, and liquid scintillation measurements were performed to obtain τ under different discrimination parameters. α and τ β Calculate τ and determine the minimum value of τ and its corresponding discrimination parameter.

[0067] 1) Preparation of α-spiked samples: Take 0.05 mL of the sample. 210 Po solution (solution matrix is ​​1 mol / L HNO3, 210 A sample with a Po activity of 6.6 Bq was placed in a 20 mL standard solution scintillation bottle, and 10 mL of type A scintillation solution was added and mixed well. This sample is designated as sample A1.

[0068] 2) Preparation of β-spiked samples: Take 0.05 mL of the sample. 90 Sr solution (solution matrix is ​​1 mol / L HNO3, in which...) 90 Sr and 90 Y is in long-term radioactive equilibrium. 90 A sample with an Sr activity of 10.1 Bq was placed in a 20 mL standard solution scintillation bottle, and 10 mL of type A scintillation solution was added and mixed well. This sample is designated as sample A2.

[0069] 3) Blank sample: Take 0.05 mL of 1 mol / L HNO3 and place it in a 20 mL standard solution scintillation bottle. Add 10 mL of type A scintillation solution and mix well. This sample is designated as sample A3.

[0070] 4) Samples A1, A2, and A3 were measured sequentially using a Hidex liquid scintillation analyzer in α / β discrimination measurement mode. The τ corresponding to different discrimination parameters (PLI) was calculated using formula (1) based on the measurement results of samples A1 and A3. α Using the measurement results of samples A2 and A3, the τ corresponding to different discrimination parameters (PLI) is calculated according to formula (2). β .

[0071] 5) Calculate τ corresponding to different discrimination parameters (PLI) according to formula (3), and plot the τ curve (e.g. Figure 1 The results show that the minimum value of τ is 1.97%, corresponding to a discrimination parameter (PLI) of 16. Therefore, the optimal discrimination parameter (PLI) under the sample preparation and instrument measurement conditions of this embodiment is 16, at which point τ... α and τ β They are 1.75% and 0.22% respectively.

[0072] Example 2: Comparison of α / β discrimination measurement performance of different scintillation fluids

[0073] Principle: Under the same sample preparation and instrument measurement conditions (except for the type of scintillation fluid), the α / β discrimination performance of different scintillation fluids is compared using the τ curve.

[0074] 1) Prepare spiked samples using type B and type C scintillation fluids according to steps 1-3 in Example 1, and designate them as group B samples (including B1, B2, B3) and group C samples (including C1, C2, C3);

[0075] 2) Measure the samples of group B and group C according to step 4 in Example 1 and calculate τ. α and τ β ; Obtain the τ curves of type B scintillation fluid and type C scintillation fluid according to step 5 in Example 1.

[0076] 3) Compare the τ curves of the three scintillation fluids A, B, and C (e.g., Figure 2 The minimum τ values ​​for types A, B, and C scintillation fluids are 1.97%, 5.08%, and 4.42%, respectively; the half-width (W) of the τ curves for types A, B, and C scintillation fluids is W. A >W B >W C Based on the minimum value of τ and the half-width at half-maximum of the τ curve, it can be determined that type A scintillation fluid has the best α / β discrimination performance, followed by type B scintillation fluid, and type C scintillation fluid has the worst performance.

[0077] Example 3: The effect of water content in the sample on α / β discrimination measurement

[0078] Principle: Samples with different water contents are measured under the same instrument conditions to investigate the effect of water on α / β discrimination measurement.

[0079] 1) Prepare three groups of samples according to steps 1-3 of Example 1, denoted as group S1, group S2, and group S3. Add 1 ml, 3 ml, and 5 ml of deionized water to samples S1, S2, and S3 respectively, and mix the samples well.

[0080] 2) Obtain the τ values ​​of samples S1, S2, and S3 according to step 4 of Example 1. α Curve (see) Figure 3 ) and τ β Curve (see) Figure 4 Samples S1, S2, and S3 τ α Curve and τ β The differences in the position of the curves indicate that increasing the water content in the sample reduces the pulse length of the α signal, but has little effect on the pulse length of the β signal. The τ values ​​for samples S1, S2, and S3... α The curve at τ α The slopes for the values ​​in the range of 15%-85% are 0.0947, 0.0997, and 0.1007 respectively; the τ values ​​for samples S1, S2, and S3 are... β The curve at τ β The slopes for values ​​ranging from 15% to 85% are -0.206, -0.215, and -0.209, respectively. The τ values ​​for samples S1, S2, and S3 are... α Curve and τ β The small difference in the slope of the curves indicates that the water content in the sample has little effect on the distribution of the scintillation signal pulse length.

[0081] Example 4: Identification of sample α signal

[0082] Principle: The convexity and concavity of the scintillation signal pulse length distribution curve are used to identify the α signal.

[0083] 1) Prepare α-spiked samples, β-spiked samples, blank samples, and mixed α and β-spiked samples under the same sample preparation conditions, denoted as D1, D2, D3, and D4. The activity ratio of β-nucleotide to α-nucleotide in sample D4 is 130:1.

[0084] 2) Obtain the scintillation signal pulse length distribution diagrams for samples D1, D2, and D3 (see...). Figure 5 The results show that the pulse length distributions of both α and β nuclide scintillation signals are unimodal, but the discrimination parameter corresponding to the peak value of the α nuclide signal is significantly higher than that of the β nuclide signal.

[0085] 3) Obtain the scintillation pulse length distribution map of sample D4 (see...). Figure 6 The results showed a distinct bulge at the tail of the scintillation signal peak, which differed significantly from the β signal distribution map, confirming the presence of α nuclides. Therefore, when the activity of β nuclides in a sample is much greater than (>100) that of α nuclides, the presence of α nuclides can be identified by the bulge at the tail of the β signal pulse length distribution map.

[0086] The device described in this invention is not limited to the embodiments described in the specific implementation. Other implementation methods derived by those skilled in the art based on the technical solution of this invention also fall within the scope of technical innovation of this invention.

Claims

1. A data processing method for α and β discrimination measurements using a liquid scintillation meter, comprising the following steps: Step S1: Obtain τ under different discrimination parameter conditions. α and τ β And calculate the total false positive rate; Step S2: Determine the optimal discrimination parameters using the total false discrimination rate; Step S3: Evaluate the α / β discrimination effect using the minimum value of the total false discrimination rate and the half-width of the curve relating the total false discrimination rate to the discrimination parameter; Step S4: Obtain the flicker signal pulse length distribution map, using τ α Curve and τ β The shift and slope of the curve indicate changes in the length of the scintillation signal pulse and its probability distribution; Step S5: Identify the α signal of the sample by analyzing the trend change of the probability distribution map of the scintillation signal pulse length.

2. The method as described in claim 1, characterized in that: In step S1, the formula for calculating the total false positive rate is: τ=τ α +t β official(3) in, τ represents the total false discrimination rate, expressed in %. τ α The percentage of α signals that are mistakenly counted as β signals, expressed in %. τ β The percentage of β signals that are mistakenly counted as α signals, expressed as a percentage.

3. The method as described in claim 1, characterized in that: In step S2, the optimal discrimination parameter corresponds to the minimum value of the total false discrimination rate.

4. The method as described in claim 1, characterized in that: in In step S3, the relationship curve between the total false discrimination rate and the discrimination parameter is denoted as the τ curve, with the discrimination parameter as the horizontal axis and the total false discrimination rate as the vertical axis.

5. The method as described in claim 1, characterized in that: in In step S3, the half-width of the curve relating the total false positive rate to the discrimination parameters is the distance between the two discrimination parameters where the total false positive rate is equal to 50%.

6. The method as described in claim 1, characterized in that: in In step S4, the slope is τ α and τ β The slope of the linearly fitted curve for all data points in the range of 15%-85%.

7. The method as described in claim 1, characterized in that: in In step S4, the flicker signal pulse length distribution map uses the discrimination parameter as the horizontal axis and the count, count rate or normalization ratio as the vertical axis.

8. The method as described in claim 1, characterized in that: In step S5, the trend change of the flash signal pulse length distribution map is the concavity and convexity of the curve.

9. The method as described in claim 1, characterized in that: in In step S5, the α signal of the sample is the count of the α signal recorded after the sample has been measured by the liquid scintillation instrument α / β.

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