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  • Steven D. Brown, Willis F. Harrington Professor

    Willis F. Harrington Professor
    University of Delaware
    239 Brown Lab
    Newark, DE 19716
    (302) 831-6861


    (b. 1950) B.S., 1972, M.S., 1975, Portland State University; Ph.D., 1978, University of Washington

    Current Research

    ​Students in my laboratory study the development of new methods for mathematical and statistical analysis of chemical data, a field known variously as chemometrics or chemo-informatics. Most of our current effort is directed to investigating problems in the field of analytical chemistry, but students in this research group are exposed to problems from a wide range of disciplines, ranging from geochemistry to biochemistry and process engineering. Our usual practice is to study real problems provided by collaborators.

    Advances in Multivariate Calibration and Classification

    ​We are devising ways to identify and compensate instrumental and background effects in data to improve mathematical methods for relating analytical responses to concentration of analytes. One current application involves developing methods for the transfer of multivariate classifications done on forensic paint samples.

    Novel Methods for Data Mining, Data Fusion and Knowledge Discovery

    ​New methods for extracting information from large amounts of data are increasingly needed as new analytical methods produce more and more data and there is simultaneously a need to focus on finer and finer detail in the instrumental response(s) from a sample, as well as the uncertainty in the multivariate analysis of the data. One current project concerns extraction of geospatial information from data sets made from a combination of isotope ratio and trace metal measurement collected from each sample. A second project aims to fuse information from multiple measurements to extract new information on complex biological samples used in the production of biopharmaceuticals.

    Representative Publications

    • D. Poerio and S.D. Brown,  A Frequency-Localized Recursive Partial Least Squares Ensemble for Soft Sensing,  J. Chemom. e2999, 2018. (DOI: 10.1002/cem.2999 )

    • D. Poerio and S.D. Brown, “Highly-Overlapped, Recursive Partial Least Squares Soft Sensor with State Partitioning via Local Variable Selection”,Chemom. Intell. Lab Syst. 175 (2018) 104–115. (DOI: 10.1016/j.chemolab.2018.02.006 )

    • C. Kneale and S.D. Brown, “Small Moving-Window Calibration Models for Soft Sensing Processes with Limited History.” Chemom. Intell. Lab. Syst.183, 2018, 36-46. (

    • C. Kneale and S.D. Brown, Band Target Entropy Minimization and Target Partial Least Squares for Spectral Recovery and Calibration, Analyt. Chim. Acta, 1031 (2018) 38-46. (DOI:10.1016/j.aca.2018.07.054 )

    • D. Poerio and S.D. Brown, Localized and Adaptive Soft Sensor Based on an Extreme Learning Machine with Automated Self-correction Strategies,  J. Chemom., 2018;e3088. ( 10.1002/cem.3088).

    • C. Kneale and S.D. Brown,  Exploratory Data Analysis using an Uncharted Forest, Talanta 189 (2018) 71–78. (DOI: 10.1016/j.talanta.2018.06.061)



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  • Chemistry and Biochemistry
  • 102 Brown Laboratory
  • University of Delaware
  • Newark, DE 19716, USA
  • Phone: 302-831-1247
  • Undergraduate Program Inquiries 302-831-2465