Basic chemometric techniques in atomic spectroscopy
著者
書誌事項
Basic chemometric techniques in atomic spectroscopy
(RSC analytical spectroscopy monographs / series editor, Neil Barnett, 13)
Royal Society of Chemistry, c2013
2nd ed
- : hbk
大学図書館所蔵 全1件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references and index
内容説明・目次
内容説明
The first edition of this book was a first book for atomic spectroscopists to present the basic principles of experimental designs, optimization and multivariate regression. Multivariate regression is a valuable statistical method for handling complex problems (such as spectral and chemical interferences) which arise during atomic spectrometry. However, the technique is underused as most spectroscopists do not have time to study the often complex literature on the subject. This practical introduction uses conceptual explanations and worked examples to give readers a clear understanding of the technique. Mathematics is kept to a minimum but, when required, is kept at a basic level. Practical considerations, interpretations and troubleshooting are emphasized and literature surveys are included to guide the reader to further work. The same dataset is used for all chapters dealing with calibration to demonstrate the differences between the different methodologies. Readers will learn how to handle spectral and chemical interferences in atomic spectrometry in a new, more efficient and cost-effective way.
目次
- An overview of atomic spectrometric techniques
- Classical linear regression by the least squares method
- Implenting a robust methodology: experimental designs and optimisation
- Ordinary multiple linear regression and principal components regression
- Partial least-squares regression
- Multivariate regression using artificial neural networks and support vector mechines
- Index
「Nielsen BookData」 より