Curve Fitting, Linear Algebra, and Solver in an Analytical Chemistry Course: A Facile and Safe Activity Suitable for the Classroom Setting

• Published in: Journal of chemical education Vol. 97; no. 4; pp. 1053 - 1060
• Main Authors: Maccione, Jesse, Welch, Joseph, Heider, Emily C
• Format: Journal Article
• Language: English
• Published: Easton American Chemical Society and Division of Chemical Education, Inc 14.04.2020; American Chemical Society
• Subjects: Algebra; Analytical chemistry; Analytical methods; Chemistry; College Students; Colleges & universities; Curricula; Curve fitting; Data analysis; Data collection; Educational activities; Learning; Learning outcomes; Linear algebra; Mathematical analysis; Matrices (mathematics); Matrix algebra; Multimedia; Organic Chemistry; Pretests Posttests; Pretreatment; Science education; Software; Statistical analysis; Statistical methods; Student Empowerment; Students; Teaching methods; Upper-Division Undergraduate; Analytical Chemistry; Applications of Chemistry; Laboratory Instruction
• ISSN: 0021-9584; 1938-1328
• DOI: 10.1021/acs.jchemed.9b00421

Undergraduate analytical chemistry courses emphasize fundamental stoichiometric and physicochemical analytical techniques with statistical analysis and linear calibrations. Higher-level data analysis techniques may not be included in the college junior-level curriculum, but widely available software enables more complex analysis to be accessible. In this work, activities to train students in multicomponent spectral curve fitting (using Microsoft Excel’s Solver) and utilizing matrix algebra were incorporated within a large-enrollment undergraduate analytical chemistry lecture setting. When analyzing multiple compounds in solutions without separation pretreatment, both curve-fitting and classical matrix approaches are valuable techniques for students to understand and execute using commercially available software. When hands-on activities, multimedia screencasts, and in-class data collection and analysis were implemented, students were trained to employ these advanced analysis methods. The efficacy of the in-class practical activities was assessed with pre- and post-test instruments that quantified gains in learning outcomes. Inclusion of such activities will empower students with an expanded repertoire of these important analytical methods and their applications with a real world, portable, active-learning approach that can be completed in a lecture setting with nonhazardous samples.