{"title":"校准。","authors":"Lane C Sander","doi":"10.6028/jres.124.027","DOIUrl":null,"url":null,"abstract":"In the context of chemical metrology, calibration is the process of relating a\n known quantity of an analyte to the corresponding measured instrumental response\n through a mathematical relationship. Calibration permits the assignment of analyte\n levels in unknown samples based on the known levels of the calibrants. Details of\n the calibration model are important to achieve accurate results. Several common\n approaches are used in calibrating methods. Most frequently, calibration models are\n based on linear instrumental response, with mathematical models that include zero\n intercept, fixed intercept, unconstrained (fitted), and bracketed models. When\n instrumental response is nonlinear, a linear model may still provide accurate\n results if the calibration range is sufficiently limited. This presentation will\n provide an overview and application of various calibration models, with\n recommendations of ways to improve measurement accuracy. Examples are presented that\n illustrate advantages and disadvantages for each of these models as applied to low\n level samples and to unknowns with levels that span several orders of\n magnitude.","PeriodicalId":54766,"journal":{"name":"Journal of Research of the National Institute of Standards and Technology","volume":"124 ","pages":"1"},"PeriodicalIF":1.3000,"publicationDate":"2019-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.6028/jres.124.027","citationCount":"0","resultStr":"{\"title\":\"Calibration.\",\"authors\":\"Lane C Sander\",\"doi\":\"10.6028/jres.124.027\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the context of chemical metrology, calibration is the process of relating a\\n known quantity of an analyte to the corresponding measured instrumental response\\n through a mathematical relationship. Calibration permits the assignment of analyte\\n levels in unknown samples based on the known levels of the calibrants. Details of\\n the calibration model are important to achieve accurate results. Several common\\n approaches are used in calibrating methods. Most frequently, calibration models are\\n based on linear instrumental response, with mathematical models that include zero\\n intercept, fixed intercept, unconstrained (fitted), and bracketed models. When\\n instrumental response is nonlinear, a linear model may still provide accurate\\n results if the calibration range is sufficiently limited. This presentation will\\n provide an overview and application of various calibration models, with\\n recommendations of ways to improve measurement accuracy. Examples are presented that\\n illustrate advantages and disadvantages for each of these models as applied to low\\n level samples and to unknowns with levels that span several orders of\\n magnitude.\",\"PeriodicalId\":54766,\"journal\":{\"name\":\"Journal of Research of the National Institute of Standards and Technology\",\"volume\":\"124 \",\"pages\":\"1\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2019-10-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.6028/jres.124.027\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Research of the National Institute of Standards and Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.6028/jres.124.027\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2019/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"INSTRUMENTS & INSTRUMENTATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Research of the National Institute of Standards and Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.6028/jres.124.027","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2019/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"INSTRUMENTS & INSTRUMENTATION","Score":null,"Total":0}
In the context of chemical metrology, calibration is the process of relating a
known quantity of an analyte to the corresponding measured instrumental response
through a mathematical relationship. Calibration permits the assignment of analyte
levels in unknown samples based on the known levels of the calibrants. Details of
the calibration model are important to achieve accurate results. Several common
approaches are used in calibrating methods. Most frequently, calibration models are
based on linear instrumental response, with mathematical models that include zero
intercept, fixed intercept, unconstrained (fitted), and bracketed models. When
instrumental response is nonlinear, a linear model may still provide accurate
results if the calibration range is sufficiently limited. This presentation will
provide an overview and application of various calibration models, with
recommendations of ways to improve measurement accuracy. Examples are presented that
illustrate advantages and disadvantages for each of these models as applied to low
level samples and to unknowns with levels that span several orders of
magnitude.
期刊介绍:
The Journal of Research of the National Institute of Standards and Technology is the flagship publication of the National Institute of Standards and Technology. It has been published under various titles and forms since 1904, with its roots as Scientific Papers issued as the Bulletin of the Bureau of Standards.
In 1928, the Scientific Papers were combined with Technologic Papers, which reported results of investigations of material and methods of testing. This new publication was titled the Bureau of Standards Journal of Research.
The Journal of Research of NIST reports NIST research and development in metrology and related fields of physical science, engineering, applied mathematics, statistics, biotechnology, information technology.