Quantification in LC and GC: A Practical Guide to Good Chromatographic Data - Tapa dura

 
9783527323012: Quantification in LC and GC: A Practical Guide to Good Chromatographic Data

Sinopsis

Closing a gap in the current literature by addressing the evaluation and quality assessment of raw data, this practice-oriented guide is clearly divided into three parts. The first describes basic considerations of chromatographic data quality, common errors and potential pitfalls in reading out and quantifying the data. Part two systematically covers the most important chromatographic methods as well as the specific requirements for obtaining good chromatographic data. The final part looks at data quality from the perspective of those regulatory authorities demanding certain standards in data quality, describing in detail best practices.
Written with the practitioner in mind, the text not only teaches the mathematical basics but also provides invaluable advice.

"Sinopsis" puede pertenecer a otra edición de este libro.

Acerca del autor

Hans-Joachim Kuss is the head of biomedical analytics at the psychiatric hospital of the Ludwig Maximilian University in Munich. He has specialised on HPLC analysis of pharmaceuticals and regularly teaches courses for students and technicians on this topic.

Stavros Kromidas works as an independent consultant for analytical chemistry. For more than 20 years he has regularly held lectures and training courses on HPLC, and has authored numerous articles and several best-selling books on various aspects of chromatography.

De la contraportada

Closing a gap in the current literature by addressing the evaluation and quality assessment of raw data, this practice-oriented guide is clearly divided into three parts. The first describes basic considerations of chromatographic data quality, common errors and potential pitfalls in reading out and quantifying the data. Part two systematically covers the most important chromatographic methods as well as the specific requirements for obtaining good chromatographic data. The final part looks at data quality from the perspective of those regulatory authorities demanding certain standards in data quality, describing in detail best practices.
Written with the practitioner in mind, the text not only teaches the mathematical basics but also provides invaluable advice.

"Sobre este título" puede pertenecer a otra edición de este libro.