17.020 (Metrology and measurement in general) 标准查询与下载

ASTM D7782-13 根据实验室内数据测定99 %/95 %临界水平 (WCL) 和可靠的检测估计 (WDE) 的标准实施规程

5.1 This practice can be used in a single laboratory for trace analysis (that is, where: 1) there are concentrations near the lower limit of the method and 2) the measurements system’s capability to discriminate analyte presence from analyte absence is of interest). In these testing situations, a reliable estimate of the minimum level at which there is confidence that detection of the analyte by the method represents true presence of the analyte in the sample is key. Where within-laboratory detection is important to data use, the WDE procedure should be used to establish the within-laboratory detection capability for each unique application of a method. 5.2 When properly applied, the WDE procedure ensures that the 998201;%/958201;% WDE has the following properties: 5.2.1 Routinely Achievable Detection—The laboratory is able to attain detection performance routinely, using studied measurement systems, without extraordinary effort, and therefore at reasonable cost. This property is needed for a detection limit to be practically useful while scientifically sound. Representative equipment and analysts must be included in the study that generates the data to calculate the WDE. 5.2.2 Inclusion of Routine Sources of Error—If appropriate data are used in calculation, the WDE practice will realistically account for sources of variation and bias common to the measurement process and routine for sample analysis. These sources include, but are not limited to: 1) intrinsic instrument noise, 2) some typical amount of carryover error, and 3) differences in analysts, sample preparation, and instruments (including signal-processing methods and software versions). 5.2.3 Exclusion of Avoidable Sources of Error—The WDE practice excludes avoidable sources of bias and variation, (that is, those which can reasonably be avoided in routine field measurements). Avoidable sources would include, but are not limited to: 1) inappropriate modifications to the method, the sample, measurement procedure, or measurement equipment, and 2) gross and easily discernible transcription errors (provided there was a way to detect and either correct or eliminate such errors in routine sample testing). 5.2.4 Low Probability of False Detection—Consistent with a measured concentration threshold (YC), the WCL is a true concentration that will provide a high probability (estimated at 998201;%) of true non-detection (and thus a low estimated probability of false detection (α) equal to 18201;%). Thus, when a sample with a real concentration of zero is measured, the probability of not detecting the analyte (that is, the probability that the measured value of the blank will be less than the WCL) would be greater than 998201;%. To be most useful, this property must be demonstrated for the particular matrix being used, and not just for reagent-grade water. 5.2.5 Low Probability of False Non-detection—Where appropriate data h......

Standard Practice for Determination of the 99 %/95 % Critical Level (WCL) and a Reliable Detection Estimate (WDE) Based on Within-laboratory Data

ASTM E2254-13 动态机械分析仪存储模数校正的标准试验方法

5.1 This test method calibrates or demonstrates conformity of a dynamic mechanical analyzer at an isothermal temperature within the range of –100 to 300°C. 5.2 Dynamic mechanical analysis experiments often use temperature ramps. This method does not address the effect of that change in temperature on the storage modulus. 5.3 A calibration factor may be required to obtain corrected storage modulus values. 5.4 This method may be used in research and development, specification acceptance, and quality control or assurance. 1.1 This test method describes the calibration or performance confirmation for the storage modulus scale of a commercial or custom built dynamic mechanical analyzer (DMA) over the temperature range of –100 to 300°C using reference materials in the range of 1 to 200 GPa. 1.2 The values stated in SI units are to be regarded as standard. No other units of measurement are included in this standard. 1.3 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety and health practices and determine the applicability of regulatory limitations prior to use.

Standard Test Method for Storage Modulus Calibration of Dynamic Mechanical Analyzers

ASTM D7783-13 根据实验室内数据测定99 %/95 %临界水平 (WCL) 和可靠的检测估计 (WDE) 的标准实施规程

5.1 Appropriate application of this practice should result in a WQE achievable by the laboratory in applying the tested method/matrix/analyte combination to routine sample analysis. That is, a laboratory should be capable of measuring concentrations greater than WQEZ8201;%, with the associated RSD equal to Z8201;% or less. 5.2 The WQE values may be used to compare the quantitation capability of different methods for analysis of the same analyte in the same matrix within the same laboratory. 5.3 The WQE procedure should be used to establish the within-laboratory quantitation capability for any application of a method in the laboratory where quantitation is important to data use. The intent of the WQE is not to impose reporting limits. The intent is to provide a reliable procedure for establishing the quantitative characteristics of the method (as implemented in the laboratory for the matrix and analyte) and thus to provide the laboratory with reliable information characterizing the uncertainty in any data produced. Then the laboratory may make informed decisions about censoring data and has the information necessary for providing reliable estimates of uncertainty with reported data. 1.1 This practice establishes a uniform standard for computing the within-laboratory quantitation estimate associated with Z % relative standard deviation (referred to herein as WQEZ8201;%), and provides guidance concerning the appropriate use and application. 1.2 WQEZ % is computed to be the lowest concentration for which a single measurement from the laboratory will have an estimated Z8201;% relative standard deviation (Z8201;% RSD, based on within-laboratory standard deviation), where Z is typically an integer multiple of 10, such as 10, 20, or 30. Z can be less than 10 but not more than 30. The WQE10 % is consistent with the quantitation approaches of Currie (1)2 and Oppenheimer, et al (2). 1.3 The fundamental assumption of the WQE is that the media tested, the concentrations tested, and the protocol followed in the developing the study data provide a representative and fair evaluation of the scope and applicability of the test method, as written. Properly applied, the WQE procedure ensures that the WQE value has the following properties: 1.3.1 Routinely Achievable WQE Value—The laboratory should be able to attain the WQE in routine analyses, using the laboratory‘s standard measurement system(s), at reasonable cost. This property is needed for a quantitation limit to be feasible in practical situations. Representative data must be used in the calculation of the WQE. 1.3.2 Accounting for Routine Sources of Error—The WQE should realistically include sources of bias and variation that are common to the measurement process and the measured materials. These sources include, but are not lim......

Standard Practice for Determination of the 99 %/95 % Critical Level (WCL) and a Reliable Detection Estimate (WDE) Based on Within-laboratory Data

ASTM F2277-12 评估可调节健身器材设计和性能特点的标准试验方法

The purpose of these test methods is to provide valid and repeatable test methods for the evaluation of selectorized strength equipment assembled and maintained according to the manufacturer's specifications. Use of these test methods in conjunction with Specification F2216 is intended to maximize the reliability of selectorized strength equipment design and reduce the risk of serious injury resulting from design deficiencies.1.1 These test methods specify procedures and apparatus used for testing and evaluating selectorized strength equipment for compliance to Specification F2216. Both design and operational parameters will be evaluated. Where possible and applicable, accepted test methods from other recognized bodies will be used and referenced. 1.2 Requirements8212;Selectorized strength equipment is to be tested in accordance with these test methods or Test Methods F2571 for all of the following parameters: 1.2.1 Stability, 1.2.2 Edge and corner sharpness, 1.2.3 Tube ends, 1.2.4 Weight stack travel, 1.2.5 Weight stack selector pin retention, 1.2.6 Function of adjustments and locking mechanisms, 1.2.7 Handgrip design and retention, 1.2.8 Assist mechanisms, 1.2.9 Foot supports, 1.2.10 Rope and belt systems: 1.2.10.1 Static load, 1.2.10.2 End fitting design, 1.2.11 Chain drive design, 1.2.12 Pulley design: 1.2.12.1 Rope pulley design, 1.2.12.2 Belt pulley design, 1.2.13 Entrapment zones, 1.2.14 Pull in points, 1.2.15 Weight stack enclosure design, 1.2.16 Loading and deflection: 1.2.16.1 Intrinsic loading and associated deflection, 1.2.16.2 Extrinsic loading and associated deflection, 1.2.16.3 Endurance loading, and 1.2.17 Documentation and warnings verification. 1.3 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety and health practices and determine the applicability of regulatory limitations prior to use.

Standard Test Methods for Evaluating Design and Performance Characteristics of Selectorized Strength Equipment

ASTM D7729-12 使用DQCALC软件测定和表示水分析测量结果精度值为相对标准偏差的标准实施规程

1.1 This Practice describes a procedure for developing a graphical model of relative standard deviation vs concentration for a analytical methods used in the analysis of water (methods that are subject to non-additive random errors) for the purpose of assigning a statement of noise or randomness to analytical results (commonly referred to as a precision statement), in either a manual or an automated fashion. 1.2 Data analysis and modeling is done with D19 Adjunct DQCALC (an Excel based tool). 1.3 The values stated in SI units are to be regarded as the standard. The values given in parentheses are for information only. 1.4 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety and health practices and determine the applicability of regulatory limitations prior to use.

Standard Practice for Determining and Expressing Precision of Measurement Results, in the Analysis of Water, as Relative Standard Deviation, Utilizing DQCALC Software

ASTM F2216-12 可选强度设备的标准规格

1.1 This specification establishes parameters for the design and manufacture of selectorized strength training equipment as defined in 3.1. 1.2 It is intended that these fitness products be used in an indoor setting or environment. 1.3 It is the intent of this standard to specify fitness products for use only by individuals age 13 and older. 1.4 This standard is to be used in conjunction with Specification F2276, Test Methods F2571, and Test Method F2277. 1.5 This standard takes precedence over Specification F2276 and Test Methods F2571 in areas that are specific or unique to selectorized strength training equipment. 1.6 The values stated in SI units are to be regarded as the standard. The values in parentheses are for information only. 1.7 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety and health practices and determine the applicability of regulatory limitations prior to use.

Standard Specification for Selectorized Strength Equipment

ASTM E2782-11 测量系统分析标准指南(MSA)

Many types of measurements are made routinely in research organizations, business and industry, and government and academic agencies. Typically, data are generated from experimental effort or as observational studies. From such data, management decisions are made that may have wide-reaching social, economic, and political impact. Data and decision making go hand in hand and that is why the quality of any measurement is importantfor data originate from a measurement process. This guide presents selected concepts and methods useful for describing and understanding the measurement process. This guide is not intended to be a comprehensive survey of this topic. Any measurement result will be said to originate from a measurement process or system. The measurement process will consist of a number of input variables and general conditions that affect the final value of the measurement. The process variables, hardware and software and their properties, and the human effort required to obtain a measurement constitute the measurement process. A measurement process will have several properties that characterize the effect of the several variables and general conditions on the measurement results. It is the properties of the measurement process that are of primary interest in any such study. The term “measurement systems analysis” or MSA study is used to describe the several methods used to characterize the measurement process. Note 18212;Sample statistics discussed in this guide are as described in Practice E2586; control chart methodologies are as described in Practice E2587.1.1 This guide presents terminology, concepts, and selected methods and formulas useful for measurement systems analysis (MSA). Measurement systems analysis may be broadly described as a body of theory and methodology that applies to the non-destructive measurement of the physical properties of manufactured objects. 1.2 Units8212;The system of units for this guide is not specified. Dimensional quantities in the guide are presented only as illustrations of calculation methods and are not binding on products or test methods treated. 1.3 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety and health practices and determine the applicability of regulatory limitations prior to use.

Standard Guide for Measurement Systems Analysis (MSA)

ASTM E2254-11 动态机械分析仪存储模数校正的标准试验方法

This test method calibrates or demonstrates conformity of a dynamic mechanical analyzer at an isothermal temperature within the range of -100 to 300 °C. Dynamic mechanical analysis experiments often use temperature ramps. This method does not address the effect of that change in temperature on the storage modulus. A calibration factor may be required to obtain corrected storage modulus values. This method may be used in research and development, specification acceptance, and quality control or assurance.1.1 This test method describes the calibration or performance confirmation for the storage modulus scale of a commercial or custom built dynamic mechanical analyzer (DMA) over the temperature range of -100 to 300 °C using reference materials in the range of 1 to 200 GPa. 1.2 The values stated in SI units are to be regarded as standard. No other units of measurement are included in this standard. 1.3 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety and health practices and determine the applicability of regulatory limitations prior to use.

Standard Test Method for Storage Modulus Calibration of Dynamic Mechanical Analyzers

ASTM E2782-11e1 测量系统分析40;MSA41的标准指南

4.1 Many types of measurements are made routinely in research organizations, business and industry, and government and academic agencies. Typically, data are generated from experimental effort or as observational studies. From such data, management decisions are made that may have wide-reaching social, economic, and political impact. Data and decision making go hand in hand and that is why the quality of any measurement is important—for data originate from a measurement process. This guide presents selected concepts and methods useful for describing and understanding the measurement process. This guide is not intended to be a comprehensive survey of this topic. 4.2 Any measurement result will be said to originate from a measurement process or system. The measurement process will consist of a number of input variables and general conditions that affect the final value of the measurement. The process variables, hardware and software and their properties, and the human effort required to obtain a measurement constitute the measurement process. A measurement process will have several properties that characterize the effect of the several variables and general conditions on the measurement results. It is the properties of the measurement process that are of primary interest in any such study. The term “measurement systems analysis” or MSA study is used to describe the several methods used to characterize the measurement process.Note 1—Sample statistics discussed in this guide are as described in Practice E2586; control chart methodologies are as described in Practice E2587. 1.1 This guide presents terminology, concepts, and selected methods and formulas useful for measurement systems analysis (MSA). Measurement systems analysis may be broadly described as a body of theory and methodology that applies to the non-destructive measurement of the physical properties of manufactured objects. 1.2 Units—The system of units for this guide is not specified. Dimensional quantities in the guide are presented only as illustrations of calculation methods and are not binding on products or test methods treated. 1.3 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety and health practices and determine the applicability of regulatory limitations prior to use.

Standard Guide for Measurement Systems Analysis 40;MSA41;

ASTM E2617-09a 经验推导的多变量校准的验证标准实践

This practice outlines a universally applicable procedure to validate the performance of a quantitative or qualitative, empirically derived, multivariate calibration relative to an accepted reference method. This practice provides procedures for evaluating the capability of a calibration to provide reliable estimations relative to an accepted reference method. This practice provides purchasers of a measurement system that incorporates an empirically derived multivariate calibration with options for specifying validation requirements to ensure that the system is capable of providing estimations with an appropriate degree of agreement with an accepted reference method. This practice provides the user of a measurement system that incorporates an empirically derived multivariate calibration with procedures capable of providing information that may be useful for ongoing quality assurance of the performance of the measurement system. Validation information obtained in the application of this practice is applicable only to the material type and property range of the materials used to perform the validation and only for the individual measurement system on which the practice is completely applied. It is the user''s responsibility to select the property levels and the compositional characteristics of the validation samples such that they are suitable to the application. This practice allows the user to write a comprehensive validation statement for the analyzer system including specific limits for the validated range of application and specific restrictions to the permitted uses of the measurement system. Users are cautioned against extrapolation of validation results beyond the material type(s) and property range(s) used to obtain these results. Users are cautioned that a validated empirically derived multivariate calibration is applicable only to samples that fall within the subset population represented in the validation set. The estimation from an empirically derived multivariate calibration can only be validated when the applicability of the calibration is explicitly established for the particular measurement for which the estimation is produced. Applicability cannot be assumed.1.1 This practice covers requirements for the validation of empirically derived calibrations (Note 1) such as calibrations derived by Multiple Linear Regression (MLR), Principal Component Regression (PCR), Partial Least Squares (PLS), Artificial Neural Networks (ANN), or any other empirical calibration technique whereby a relationship is postulated between a set of variables measured for a given sample under test and one or more physical, chemical, quality, or membership properties applicable to that sample. Note 18212;Empirically derived calibrations are sometimes referred to as “models” or “calibrations.” In the following text, for conciseness, the term “calibration” may be used instead of the full name of the procedure. 1.2 This practice does not cover procedures for establishing said postulated relationship. 1.3 This practice serves as an overview of techniques used to verify the applicability of an empirically derived multivariate calibration to the measurement of a sample under test and to verify equivalence between the properties calculated from the empirically derived multivariate calibration and the results of an accepted reference method of measurement to within control limits established for the prespecified statistical confidence level. 1.4 This standard does not purport to address all of the safety con......

Standard Practice for Validation of Empirically Derived Multivariate Calibrations

ASTM E2617-09 经验推导的多变量校准的验证标准实践

This practice outlines a universally applicable procedure to validate the performance of a quantitative or qualitative, empirically derived, multivariate calibration relative to an accepted reference method. This practice provides procedures for evaluating the capability of a calibration to provide reliable estimations relative to an accepted reference method. This practice provides purchasers of a measurement system that incorporates an empirically derived multivariate calibration with options for specifying validation requirements to ensure that the system is capable of providing estimations with an appropriate degree of agreement with an accepted reference method. This practice provides the user of a measurement system that incorporates an empirically derived multivariate calibration with procedures capable of providing information that may be useful for ongoing quality assurance of the performance of the measurement system. Validation information obtained in the application of this practice is applicable only to the material type and property range of the materials used to perform the validation and only for the individual measurement system on which the practice is completely applied. It is the user''s responsibility to select the property levels and the compositional characteristics of the validation samples such that they are suitable to the application. This practice allows the user to write a comprehensive validation statement for the analyzer system including specific limits for the validated range of application and specific restrictions to the permitted uses of the measurement system. Users are cautioned against extrapolation of validation results beyond the material type(s) and property range(s) used to obtain these results. Users are cautioned that a validated empirically derived multivariate calibration is applicable only to samples that fall within the subset population represented in the validation set. The estimation from an empirically derived multivariate calibration can only be validated when the applicability of the calibration is explicitly established for the particular measurement for which the estimation is produced. Applicability cannot be assumed.1.1 This practice covers requirements for the validation of empirically derived calibrations (Note 1) such as calibrations derived by Multiple Linear Regression (MLR), Principal Component Regression (PCR), Partial Least Squares (PLS), Artificial Neural Networks (ANN), or any other empirical calibration technique whereby a relationship is postulated between a set of variables measured for a given sample under test and one or more physical, chemical, quality, or membership properties applicable to that sample. Note 18212;Empirically derived calibrations are sometimes referred to as “models” or “calibrations.” In the following text, for conciseness, the term “calibration” may be used instead of the full name of the procedure. 1.2 This practice does not cover procedures for establishing said postulated relationship. 1.3 This practice serves as an overview of techniques used to verify the applicability of an empirically derived multivariate calibration to the measurement of a sample under test and to verify equivalence between the properties calculated from the empirically derived multivariate calibration and the results of an accepted reference method of measurement to within control limits established for the prespecified statistical confidence level. 1.4 This standard does not purport to address all of the safety con......

Standard Practice for Validation of Empirically Derived Multivariate Calibrations

ASTM E2617-08 经验推导的多变量校准的验证标准实践

This practice outlines a universally applicable procedure to validate the performance of a quantitative or qualitative, empirically derived, multivariate calibration relative to an accepted reference method. This practice provides procedures for evaluating the capability of a calibration to provide reliable estimations relative to an accepted reference method. This practice provides purchasers of a measurement system that incorporates an empirically derived multivariate calibration with options for specifying validation requirements to ensure that the system is capable of providing estimations with an appropriate degree of agreement with an accepted reference method. This practice provides the user of a measurement system that incorporates an empirically derived multivariate calibration with procedures capable of providing information that may be useful for ongoing quality assurance of the performance of the measurement system. Validation information obtained in the application of this practice is applicable only to the material type and property range of the materials used to perform the validation and only for the individual measurement system on which the practice is completely applied. It is the user''s responsibility to select the property levels and the compositional characteristics of the validation samples such that they are suitable to the application. This practice allows the user to write a comprehensive validation statement for the analyzer system including specific limits for the validated range of application and specific restrictions to the permitted uses of the measurement system. Users are cautioned against extrapolation of validation results beyond the material type(s) and property range(s) used to obtain these results. Users are cautioned that a validated empirically derived multivariate calibration is applicable only to samples that fall within the subset population represented in the validation set. The estimation from an empirically derived multivariate calibration can only be validated when the applicability of the calibration is explicitly established for the particular measurement for which the estimation is produced. Applicability cannot be assumed.1.1 This practice covers requirements for the validation of empirically derived calibrations (Note 1) such as calibrations derived by Multiple Linear Regression (MLR), Principal Component Regression (PCR), Partial Least Squares (PLS), Artificial Neural Networks (ANN), or any other empirical calibration technique whereby a relationship is postulated between a set of variables measured for a given sample under test and one or more physical, chemical, quality, or membership properties applicable to that sample. Note 18212;Empirically derived calibrations are sometimes referred to as “models” or “calibrations.” In the following text, for conciseness, the term “calibration” may be used instead of the full name of the procedure. 1.2 This practice does not cover procedures for establishing said postulated relationship. 1.3 This practice serves as an overview of techniques used to verify the applicability of an empirically derived multivariate calibration to the measurement of a sample under test and to verify equivalence between the properties calculated from the empirically derived multivariate calibration and the results of an accepted reference method of measurement to within control limits established for the prespecified statistical confidence level. 1.4 This standard does not purport to address all of the safety concerns, if an......

Standard Practice for Validation of Empirically Derived Multivariate Calibrations

ASTM E2617-08ae1 经验推导的多变量校准的验证标准实践

This practice outlines a universally applicable procedure to validate the performance of a quantitative or qualitative, empirically derived, multivariate calibration relative to an accepted reference method. This practice provides procedures for evaluating the capability of a calibration to provide reliable estimations relative to an accepted reference method. This practice provides purchasers of a measurement system that incorporates an empirically derived multivariate calibration with options for specifying validation requirements to ensure that the system is capable of providing estimations with an appropriate degree of agreement with an accepted reference method. This practice provides the user of a measurement system that incorporates an empirically derived multivariate calibration with procedures capable of providing information that may be useful for ongoing quality assurance of the performance of the measurement system. Validation information obtained in the application of this practice is applicable only to the material type and property range of the materials used to perform the validation and only for the individual measurement system on which the practice is completely applied. It is the user''s responsibility to select the property levels and the compositional characteristics of the validation samples such that they are suitable to the application. This practice allows the user to write a comprehensive validation statement for the analyzer system including specific limits for the validated range of application and specific restrictions to the permitted uses of the measurement system. Users are cautioned against extrapolation of validation results beyond the material type(s) and property range(s) used to obtain these results. Users are cautioned that a validated empirically derived multivariate calibration is applicable only to samples that fall within the subset population represented in the validation set. The estimation from an empirically derived multivariate calibration can only be validated when the applicability of the calibration is explicitly established for the particular measurement for which the estimation is produced. Applicability cannot be assumed.1.1 This practice covers requirements for the validation of empirically derived calibrations (Note 1) such as calibrations derived by Multiple Linear Regression (MLR), Principal Component Regression (PCR), Partial Least Squares (PLS), Artificial Neural Networks (ANN), or any other empirical calibration technique whereby a relationship is postulated between a set of variables measured for a given sample under test and one or more physical, chemical, quality, or membership properties applicable to that sample. Note 18212;Empirically derived calibrations are sometimes referred to as “models” or “calibrations.” In the following text, for conciseness, the term “calibration” may be used instead of the full name of the procedure. 1.2 This practice does not cover procedures for establishing said postulated relationship. 1.3 This practice serves as an overview of techniques used to verify the applicability of an empirically derived multivariate calibration to the measurement of a sample under test and to verify equivalence between the properties calculated from the empirically derived multivariate calibration and the results of an accepted reference method of measurement to within control limits established for the prespecified statistical confidence level. 1.4 This standard does not purport to address all of the safety con......

Standard Practice for Validation of Empirically Derived Multivariate Calibrations

ASTM E2617-08a 经验推导的多变量校准的验证标准实践

This practice outlines a universally applicable procedure to validate the performance of a quantitative or qualitative, empirically derived, multivariate calibration relative to an accepted reference method. This practice provides procedures for evaluating the capability of a calibration to provide reliable estimations relative to an accepted reference method. This practice provides purchasers of a measurement system that incorporates an empirically derived multivariate calibration with options for specifying validation requirements to ensure that the system is capable of providing estimations with an appropriate degree of agreement with an accepted reference method. This practice provides the user of a measurement system that incorporates an empirically derived multivariate calibration with procedures capable of providing information that may be useful for ongoing quality assurance of the performance of the measurement system. Validation information obtained in the application of this practice is applicable only to the material type and property range of the materials used to perform the validation and only for the individual measurement system on which the practice is completely applied. It is the user''s responsibility to select the property levels and the compositional characteristics of the validation samples such that they are suitable to the application. This practice allows the user to write a comprehensive validation statement for the analyzer system including specific limits for the validated range of application and specific restrictions to the permitted uses of the measurement system. Users are cautioned against extrapolation of validation results beyond the material type(s) and property range(s) used to obtain these results. Users are cautioned that a validated empirically derived multivariate calibration is applicable only to samples that fall within the subset population represented in the validation set. The estimation from an empirically derived multivariate calibration can only be validated when the applicability of the calibration is explicitly established for the particular measurement for which the estimation is produced. Applicability cannot be assumed.1.1 This practice covers requirements for the validation of empirically derived calibrations (Note 1) such as calibrations derived by Multiple Linear Regression (MLR), Principal Component Regression (PCR), Partial Least Squares (PLS), Artificial Neural Networks (ANN), or any other empirical calibration technique whereby a relationship is postulated between a set of variables measured for a given sample under test and one or more physical, chemical, quality, or membership properties applicable to that sample. Note 18212;Empirically derived calibrations are sometimes referred to as “models” or “calibrations.” In the following text, for conciseness, the term “calibration” may be used instead of the full name of the procedure. 1.2 This practice does not cover procedures for establishing said postulated relationship. 1.3 This practice serves as an overview of techniques used to verify the applicability of an empirically derived multivariate calibration to the measurement of a sample under test and to verify equivalence between the properties calculated from the empirically derived multivariate calibration and the results of an accepted reference method of measurement to within control limits established for the prespecified statistical confidence level. 1.4 This standard does not purport to address all of the safety concerns, if an......

Standard Practice for Validation of Empirically Derived Multivariate Calibrations

ASTM E2254-08 动态机械分析仪储存模量校准的标准试验方法

This test method calibrates or demonstrates conformity of a dynamic mechanical analyzer at an isothermal temperature within the range of -100 to 300 °C. Dynamic mechanical analysis experiments often use temperature ramps. This method does not address the effect of that change in temperature on the storage modulus. A calibration factor may be required to obtain corrected storage modulus values. This method may be used in research and development, specification acceptance, and quality control or assurance.1.1 This test method describes the calibration or performance confirmation for the storage modulus scale of a commercial or custom built dynamic mechanical analyzer (DMA) over the temperature range of -100 to 300 °C using reference materials in the range of 1 to 200 GPa. 1.2 SI units are the standard. 1.3 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety and health practices and determine the applicability of regulatory limitations prior to use.

Standard Test Method for Storage Modulus Calibration of Dynamic Mechanical Analyzers

ASTM D6091-07 带可忽略校准误差的分析方法用99%95%试验室间检测评估(IDE)的标准实施规程

Appropriate application of this practice should result in an IDE achievable by most laboratories properly using the test method studied. This IDE provides the basis for any prospective use of the test method by qualified laboratories for reliable detection of low-level concentrations of the same analyte as the one studied in this practice and same media (matrix). The IDE values may be used to compare the detection power of different methods for analysis of the same analyte in the same matrix. The IDE provides high probability (approximately 95 %) that result values of the method studied which exceed the IDE represent presence of analyte in the sample and high probability (approximately 99 %) that blank samples will not result in a detection. The IDE procedure should be used to establish the interlaboratory detection capability for any application of a method where interlaboratory detection is important to data use. The intent of IDE is not to set reporting limits.1.1 This practice establishes a standard for computing a 99 %/95 % Interlaboratory Detection Estimate (IDE) and provides guidance concerning the appropriate use and application. The calculations involved in this practice can be performed with DQCALC, Microsoft Excel-based software available from ASTM.1.2 The IDE is computed to be the lowest concentration at which there is 90 % confidence that a single measurement from a laboratory selected from the population of qualified laboratories represented in an interlaboratory study will have a true detection probability of at least 95 % and a true nondetection probability of at least 99 % (when measuring a blank sample).1.3 The fundamental assumption of the collaborative study is that the media tested, the concentrations tested, and the protocol followed in the study provide a representative and fair evaluation of the scope and applicability of the test method as written. When properly applied, the IDE procedure ensures that the 99 %/95 % IDE has the following properties:1.3.1 Routinely Achievable IDE Value8212;Most laboratories are able to attain the IDE detection performance in routine analyses, using a standard measurement system, at reasonable cost. This property is needed for a detection limit to be practically feasible. Representative laboratories must be included in the data to calculate the IDE.1.3.2 Routine Sources of Error Accounted for8212;The IDE should realistically include sources of bias and variation which are common to the measurement process. These sources include, but are not limited to: intrinsic instrument noise, some typical amount of carryover error, plus differences in laboratories, analysts, sample preparation, and instruments.1.3.3 Avoidable Sources of Error Excluded8212;The IDE should realistically exclude avoidable sources of bias and variation, that is, those which can reasonably be avoided in routine field measurements. Avoidable sources would include, but are not limited to: modifications to the sample, measurement procedure, or measurement equipment of the validated method, and gross and easily discernible transcription errors (provided there was a way to detect and either correct or eliminate them).1.3.4 Low Probability of False Detection8212;The IDE is a true concentration consistent with a measured concentration threshold (critical measured value) that will provide a high probability, 99 %, of true nondetection (a low probability of false detection, 945; = 1 %). Thus, when measuring a blank sample, the probability of not detecting the analyte would be 99 %. To be useful, this must be demonstrated for the particular matrix being used, and not just for reagent water.1.3.5 Low Probability of False Nondetection8212;The IDE should be a true concentration at which there is a high probability, at least 95 %, of true detection (a......

Standard Practice for 99 %/95 % Interlaboratory Detection Estimate (IDE) for Analytical Methods with Negligible Calibration Error

ASTM D6091-07(2014) 存在可忽略不计校准误差的分析方法用99%/95%实验室间检测评估40;IDE41的标准实施规程

5.1 Appropriate application of this practice should result in an IDE achievable by most laboratories properly using the test method studied. This IDE provides the basis for any prospective use of the test method by qualified laboratories for reliable detection of low-level concentrations of the same analyte as the one studied in this practice and same media (matrix). 5.2 The IDE values may be used to compare the detection power of different methods for analysis of the same analyte in the same matrix. 5.3 The IDE provides high probability (approximately 958201;%) that result values of the method studied which exceed the IDE represent presence of analyte in the sample and high probability (approximately 998201;%) that blank samples will not result in a detection. 5.4 The IDE procedure should be used to establish the interlaboratory detection capability for any application of a method where interlaboratory detection is important to data use. The intent of IDE is not to set reporting limits. 1.1 This practice establishes a standard for computing a 998201;%/958201;% Interlaboratory Detection Estimate (IDE) and provides guidance concerning the appropriate use and application. The calculations involved in this practice can be performed with DQCALC, Microsoft Excel-based software available from ASTM.2 1.2 The IDE is computed to be the lowest concentration at which there is 908201;% confidence that a single measurement from a laboratory selected from the population of qualified laboratories represented in an interlaboratory study will have a true detection probability of at least 958201;% and a true nondetection probability of at least 998201;% (when measuring a blank sample). 1.3 The fundamental assumption of the collaborative study is that the media tested, the concentrations tested, and the protocol followed in the study provide a representative and fair evaluation of the scope and applicability of the test method as written. When properly applied, the IDE procedure ensures that the 998201;%/958201;% IDE has the following properties: 1.3.1 Routinely Achievable IDE Value—Most laboratories are able to attain the IDE detection performance in routine analyses, using a standard measurement system, at reasonable cost. This property is needed for a detection limit to be practically feasible. Representative laboratories must be included in the data to calculate the IDE. 1.3.2 Routine Sources of Error Accounted For—The IDE should realistically include sources of bias and variation which are common to the measurement process. These sources include, but are not limited to: intrinsic instrument noise, some typical amount of carryover error, plus differences in laboratories, analysts, sample preparation, and instruments. 1.3.3 Avoidable Sources of Error Excluded—The IDE should realistically exclude avoidable sources of bias and variation, that is, those which can reasonably be avoided in routine field measurements. Avoidable sources would include, but are not limited to: modifications to the sample, measurement procedure, or measurement equipment of the validated method, and gross and easily discernible transcription errors (provided there was a way to detect and either correct or eliminate them).

Standard Practice for 99?%/95?% Interlaboratory Detection Estimate 40;IDE41; for Analytical Methods with Negligible Calibration Error

ASTM F2537-06 用于测量微移动用直线位移传感器系统的刻度标准实施规范

Linear displacement sensor systems play an important role in orthopedic applications to measure micromotion during simulated use of joint prostheses. Linear displacement sensor systems must be calibrated for use in the laboratory to ensure reliable conversions of the system’s electrical output to engineering units. Linear displacement sensor systems should be calibrated before initial use, at least annually thereafter, after any change in the electronic configuration that employs the sensor, after any significant change in test conditions using the sensor that differ from conditions during the last calibration, and after any physical action on the sensor that might affect its response. Verification of sensor performance in accordance with calibration should be performed on a per use basis both before and after testing. Such verification can be done with a less accurate standard than that used for calibration, and may be done with only a few points. Linear displacement sensor systems generally have a working range within which voltage output is linearly proportional to displacement of the sensor. This procedure is applicable to the linear range of the sensor. Recommended practice is to use the linear displacement sensor system only within its linear working range.1.1 This practice covers the procedures for calibration of linear displacement sensors and their corresponding power supply, signal conditioner, and data acquisition systems (linear displacement sensor systems) for use in measuring micromotion. It covers any sensor used to measure displacement that gives an electrical voltage output that is linearly proportional to displacement. This includes, but is not limited to, linear variable differential transformers (LVDTs) and differential variable reluctance transducers (DVRTs).1.2 This calibration procedure is used to determine the relationship between output of the linear displacement sensor system and displacement. This relationship is used to convert readings from the linear displacement sensor system into engineering units.1.3 This calibration procedure is also used to determine the error of the linear displacement sensor system over the range of its use.This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety and health practices and determine the applicability of regulatory limitations prior to use.

Standard Practice for Calibration of Linear Displacement Sensor Systems Used to Measure Micromotion

ASTM F2216-05 可选强度设备的标准规范

1.1 This specification establishes guidelines for the design and manufacture of selectorized strength equipment as defined in 3.1.6, 3.1.16, and 3.1.29.1.2 It is the intent of this specification to specify products for use by individuals age 12 and above.1.3 This specification shall be used with its accompanying test method, Test Method F 2277.1.4 The values stated in SI units are to be regarded as the standard. The values in parentheses are for information only.1.5 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety and health practices and determine the applicability of regulatory limitations prior to use.

Standard Specification for Selectorized Strength Equipment

ASTM D6091-03 可忽略校准误差的分析方法用99%/95%实验室间检测评估的标准实施规范

1.1 This practice establishes a standard for computing a 99 %/95 % Interlaboratory Detection Estimate (IDE) and provides guidance concerning the appropriate use and application. 1.2 The IDE is computed to be the lowest concentration at which there is 90 % confidence that a single measurement from a laboratory selected from the population of qualified laboratories represented in an interlaboratory study will have a true detection probability of at least 95 % and a true nondetection probability of at least 99 % (when measuring a blank sample). 1.3 The fundamental assumption of the collaborative study is that the media tested, the concentrations tested, and the protocol followed in the study provide a representative and fair evaluation of the scope and applicability of the test method as written. When properly applied, the IDE procedure ensures that the 99 %/95 % IDE has the following properties: 1.3.1 Routinely Achievable IDE Value-Most laboratories are able to attain the IDE detection performance in routine analyses, using a standard measurement system, at reasonable cost. This property is needed for a detection limit to be practically feasible. Representative laboratories must be included in the data to calculate the IDE. 1.3.2 Routine Sources of Error Accounted for-The IDE should realistically include sources of bias and variation which are common to the measurement process. These sources include, but are not limited to: instrinsic instrument noise, some typical amount of carryover error, plus differences in laboratories, analysts, sample preparation, and instruments. 1.3.3 Avoidable Sources of Error Excluded- The IDE should realistically exclude avoidable sources of bias and variation, that is, those which can reasonably be avoided in routine field measurements. Avoidable sources would include, but are not limited to: modification to the sample, measurement procedure, or measurement equipment of the validated method, and gross and easily discernable transcription errors (provided there was a way to detect and either correct or eliminate them). 1.3.4 Low Probability of False Detection-The IDE is a true concentration consistent with a measured concentration threshold (critical measured value) that will provide a high probability, 99 %, of true nondetection (a low probability of false detection, alpha = 1 %). Thus, when measuring a blank sample, the probability of not detecting the analyte would be 99 %. To be useful, this must be demonstrated for the particular matrix being use, and not just for reagent water. 1.3.5 Low Probability of False Nondetection- The IDE should be a true concentration at which there is a high probability, at least 95 %, of true detection (a low probability of false nondetection, beta = 5 %, at the IDE), with a simultaneous low probability of false detection (see 1.3.4). Thus, when measuring a sample at the IDE, the probability of detection would be at least 95 %. To be useful, this must be demonstrated for the particular matrix being used, and not just for reagent water. Note 1-The referenced probabilities, alpha and beta, are key parameters for risk-based assessment of a detection limit. 1.4 The IDE applies to measurement methods for which calibration error is minor relative to other sources, such as when the dominant source of variation is one of the following (with comment): 1.4.1 Sample Preparation, and calibration standards do not have to go through sample preparation. 1.4.2 Differences in Analysis, and analysts have little opportunity to affect calibration results (such as with automated calibration). 1.4.3 Differences in Laboratories, for whatever reasons, perhaps difficult to identify and elimate. 1.4.4 Differences in Instruments (measurement equipment), which could take the form of differences in manufacturer, model, hardware, electronics, sampling rate, chemical processing rate, integration t......

Standard Practice for 99 %/95 % Interlaboratory Detection Estimate (IDE) for Analytical Methods with Negligible Calibration Error