首页> 外文期刊>The Annals of Occupational Hygiene >CHEMOMETRICS IN OCCUPATIONAL HYGIENE—HOW AND WHY! A PICTURE CAN TELL MORE THAN A THOUSAND WORDS AND FIGURES!
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CHEMOMETRICS IN OCCUPATIONAL HYGIENE—HOW AND WHY! A PICTURE CAN TELL MORE THAN A THOUSAND WORDS AND FIGURES!

机译:职业卫生中的化学方法-为什么和为什么!图片可以说成千上万个单词和数字!

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摘要

In this introductory article the author argues for an increased use of a multivariate analytical approach to the complex problems encountered in occupational hygiene. Relations between exposure at the work place and reported health effects are mostly so complicated and depend on so many factors that methods other than the traditional statistical techniques should be applied. Chemometrics is a field within chemistry where mathematics, statistics and modern computer technology are used to perform multidimensional data analysis. Graphical plots are extensively used to extract the most relevant information from the measurements. The possibility of performing soft modelling through pattern recognition and multifactorial regression analysis will simplify the management of large data sets. A 'metric' philosophy is introduced to describe similarity and dissimilarity among many objects characterized with many variables. This article emphasizes the use of principal component analysis and partial least-squares regression for such purposes. Application of the SIMCA method for classification of objects is also described. These methods are not dependent upon a priori formulated hypotheses, as in the classical modelling techniques. Instead of being restricted to accepting or rejecting previously formulated hypotheses, these methods may lead to new insights and unperceived features of a complex problem. The application of such exploratory methods may produce new hypotheses and further investigations are necessary to confirm or discard any 'new' chemometric findings.
机译:在这篇介绍性文章中,作者主张增加使用多元分析方法来解决职业卫生中遇到的复杂问题。工作场所的暴露与所报告的健康影响之间的关系非常复杂,并且取决于许多因素,因此应采用除传统统计技术以外的方法。化学计量学是化学领域中的一个领域,其中数学,统计和现代计算机技术用于执行多维数据分析。图形化绘图被广泛用于从测量中提取最相关的信息。通过模式识别和多因素回归分析执行软建模的可能性将简化大数据集的管理。引入了“度量”原理来描述具有许多变量特征的许多对象之间的相似性和不相似性。本文强调将主成分分析和偏最小二乘回归用于此类目的。还描述了SIMCA方法在对象分类中的应用。这些方法不像传统建模技术那样依赖先验公式化的假设。这些方法不仅限于接受或拒绝先前提出的假设,还可以带来新的见解和复杂问题的无法理解的特征。这种探索性方法的应用可能会产生新的假设,需要进一步研究以确认或丢弃任何“新”化学计量学发现。

著录项

  • 来源
    《The Annals of Occupational Hygiene》 |1996年第2期|p.145-169|共25页
  • 作者

    Erik Bye;

  • 作者单位

    National Institute of Occupational Health, P.O. Box 8149 Dep., 0033 Oslo, Norway;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 劳动卫生;
  • 关键词

  • 入库时间 2022-08-18 00:23:52

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