In the Classroom www.JCE.DivCHED.org • Vol. 82 No. 3 March 2005 • Journal of Chemical Education 415 Most of today’s undergraduate chemistry laboratories are equipped with computerized instruments. The instrument– computer interface provides the user with an opportunity to acquire and store large quantities of data quickly (allowing chemical processes to be monitored in real time) and to re- trieve and post-analyze data for further interpretation. Such instruments include ultraviolet–visible (UV–vis), high-reso- lution infrared (IR), and flame atomic absorption (FAAS) spectrometers, gas chromatography (GC), and high-perfor- mance liquid chromatography (HPLC) equipped with dif- ferent types of detectors, gas chromatography–mass spectrometers (GC–MS), and potentiostats for studying elec- trochemical processes. Except for FAAS, the majority of these instruments are first-order type, suitable for simultaneously detecting different compounds in a multicomponent sample. A challenging problem in analyzing a multicomponent system is establishing optimum conditions for all compounds of interest. Two or more compounds may have similar re- sponse features, leading to highly correlated signals. Several variables in the sample matrix may affect the responses of the compounds differently, making it even more difficult to detect the compounds simultaneously. These problems can be partially solved through the application of more advanced data-analysis techniques, beyond the use of a least-squares method for finding the “best fit” using a straight-line model. Students need to be equipped with more advanced skills of not only analyzing data, but also of designing experiments and optimizing the experimental parameters that will lead to good data. Chemometrics techniques (1–3) are important tools for enhancing such needed skills. Chemometrics Teaching chemometrics at the undergraduate level has been a topic of national discussion since the early 1980s (4– 9). For instance, what topics must be included and to what depth these topics must be covered are important questions, along with the demand on the mathematics related to ch- emometrics techniques. Delaney and Warren (4) proposed a chemometrics content to include simplex optimization, data smoothing, pattern recognition, library search, graph theory, and factor analysis, among others. Some of these topics are shared in chemistry courses and other disciplines. For ex- ample, in our department, students taking chemical litera- ture courses delve into library searches. In other disciplines such as computer science, students learn about graph theory. Some institutions, especially graduate institutions, offer ch- emometrics as a full-semester course, thus allowing instruc- tors to treat chemometrics techniques with a broader spectrum and greater depth. At the undergraduate level, re- strictions on the number of credit hours and the desire to introduce students to a broad spectrum of techniques all place constraints on whether to have a full-semester course of ch- emometrics. Notwithstanding these challenges, more and more undergraduate institutions are teaching chemometrics in their classes (7–9). This article describes a chemometrics module that has been developed for senior-level students taking instrumental analysis chemistry at Kennesaw State University. The mod- ule has been engineered for a wide spectrum of the student body. Abstract concepts have been simplified by use of illus- trative examples and graphics, while demonstrating the ca- pabilities of chemometrics. The initial activities are spread over the first four weeks of the semester, followed by further applications during the rest of the semester. The module is totally integrated with the instrumental analysis topics of- fered, namely, spectroscopy, separation science, and elec- troanalysis. In the first four weeks of the semester topics in linear algebra, leading to the matrix formulation of the classical least-squares method, are reviewed. The F test and its appli- cations in the analysis of variances (ANOVA) in data model- ing are presented, followed by the theory and applications of multivariate analysis techniques (multiple linear regression, MLR, and target factor analysis, TFA). Fast Fourier trans- form (FFT) is used to demonstrate how digital filtering can enhance the signal-to-noise ratio, hence improve detection limits. Students are provided with synthetic and experimen- tal data to prepare file structures that are compatible with the computer programs being used. Students also learn how to run the computer programs and to interpret their obser- vations. As the semester progresses, a number of labs are per- formed to reinforce the skills that have been learned. For ex- ample, students determine iron as a phenanthroline complex in centrum tablets via an external standard. They repeat the experiment using the standard addition method and then analyze their data to establish whether there is interference from the centrum tablet matrix. In another lab, TFA is used to identify transition-metal ions (Co 2+ , Cu 2+ , Ni 2+ , MnO 4 − , Cr 2 O 7 2− ) in a mixture by extracting their absorptivities. An- other experiment, developed by directed-study students for this course, involves the analysis of pain relievers for aspirin, caffeine, and salicylic acid by TFA (10). The same system is analyzed by HPLC–UV. The two methods are compared by use of ANOVA and the appropriate F tests. Computer pro- grams for data analysis were locally coded in Turbo Pascal, which is compatible with MS-DOS. Similar programs can be coded using Matlab (1, 11), which is compatible with the Windows environment. Some highlights of the activities of this module are presented. Pertinent equations for ANOVA are listed in the Supplemental Material. W In this article we have used TFA to illustrate multivariate-analysis techniques. Other techniques (principal component regression, PCR, A Chemometrics Module for an Undergraduate W Instrumental Analysis Chemistry Course Huggins Z. Msimanga,* Phet Elkins, Segmia K. Tata, and Dustin Ryan Smith Department of Chemistry and Biochemistry, Kennesaw State University, Kennesaw, GA 30144; *hmsimang@kennesaw.edu