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Title: Experimental data analysis: An algorithm for determining rates and smoothing data

Abstract

Reaction rate determination from experimental data is generally an essential part of evaluating enzyme or microorganism growth kinetics and the effects on them. Commonly used methods include forward, centered, or backward finite difference equations using two or more data points. Another commonly applied method for determining rates is least-square regression techniques, and when the sought function is unknown, polynomials are often applied to represent the data. The cubic spline functions presented in this article represent a versatile method of evaluating rates. The advantage in using this method is that experimental error may be largely accounted for by the incorporation of a smoothing step of the experimental data without force-fitting of the data. It also works well when data are unevenly spaced (often the case for experiments running over long periods of time). The functions are easily manipulated, and the algorithm can be written concisely for computer programming. The development of spline functions to determine derivatives as well as integrals is presented. 5 refs., 5 figs.

Authors:
 [1]
  1. Oak Ridge National Lab., TN (United States)
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
576239
Report Number(s):
CONF-960539-
Journal ID: ABIBDL; ISSN 0273-2289; TRN: 98:000980-0019
DOE Contract Number:  
AC05-96OR22464
Resource Type:
Journal Article
Journal Name:
Applied Biochemistry and Biotechnology
Additional Journal Information:
Journal Volume: 63-65; Conference: 18. symposium on biotechnology for fuels and chemicals, Gatlinburg, TN (United States), 5-9 May 1996; Other Information: PBD: Spr 1997
Country of Publication:
United States
Language:
English
Subject:
55 BIOLOGY AND MEDICINE, BASIC STUDIES; 99 MATHEMATICS, COMPUTERS, INFORMATION SCIENCE, MANAGEMENT, LAW, MISCELLANEOUS; METABOLISM; EQUATIONS; INTEGRALS; POLYNOMIALS; SPLINE FUNCTIONS; MICROORGANISMS; GROWTH

Citation Formats

Klasson, K T. Experimental data analysis: An algorithm for determining rates and smoothing data. United States: N. p., 1997. Web. doi:10.1007/BF02920435.
Klasson, K T. Experimental data analysis: An algorithm for determining rates and smoothing data. United States. https://doi.org/10.1007/BF02920435
Klasson, K T. 1997. "Experimental data analysis: An algorithm for determining rates and smoothing data". United States. https://doi.org/10.1007/BF02920435.
@article{osti_576239,
title = {Experimental data analysis: An algorithm for determining rates and smoothing data},
author = {Klasson, K T},
abstractNote = {Reaction rate determination from experimental data is generally an essential part of evaluating enzyme or microorganism growth kinetics and the effects on them. Commonly used methods include forward, centered, or backward finite difference equations using two or more data points. Another commonly applied method for determining rates is least-square regression techniques, and when the sought function is unknown, polynomials are often applied to represent the data. The cubic spline functions presented in this article represent a versatile method of evaluating rates. The advantage in using this method is that experimental error may be largely accounted for by the incorporation of a smoothing step of the experimental data without force-fitting of the data. It also works well when data are unevenly spaced (often the case for experiments running over long periods of time). The functions are easily manipulated, and the algorithm can be written concisely for computer programming. The development of spline functions to determine derivatives as well as integrals is presented. 5 refs., 5 figs.},
doi = {10.1007/BF02920435},
url = {https://www.osti.gov/biblio/576239}, journal = {Applied Biochemistry and Biotechnology},
number = ,
volume = 63-65,
place = {United States},
year = {Wed Dec 31 00:00:00 EST 1997},
month = {Wed Dec 31 00:00:00 EST 1997}
}