(Office Hours: Sims 312A TR 4:00-4:50)

Course Text: Chemometrics: Data Analysis for the Laboratory and Chemical Plant, Richard G. Brereton, 2003.

Course Outline: This is a short course in Chemometrics, the application of mathematical and statistical techniques for the analysis of chemical data sets.  With the tremendous increase in data collection and processing capabilities, the rate of data generation using modern analytical instruments can be overwhelming.  For example, a GC/FT-IR system can collect ten to twenty 4096 point interferograms each second and a single GC run may require 30-45 minutes.  The resulting large data set of interferograms must be mathematically processed to identify which correspond to eluting GC peaks, to determine the reference and sample power spectra, and then to determine the infrared absorbance spectra so that each peak can be identified and quantified.  The goal of chemometrics is to provide knowledge and understanding from large data sets.  For GC/FT-IR analysis, chemometrics provides the tools which take the raw set of unrecognizable interferograms and transform them into a printout listing all substances found in the sample and the amount of each that is present.  Chemometrics rescues us from the situation in which we are drowning in information but starving for knowledge.

The goal of many chemometric techniques is to use measurements to produce a model for any one of a nearly infinite number of possibilities to include defining a complex system, predicting properties, optimizing a signal, designing an experiment, immediately assessing the quality of a product from an industrial process or  proving an important hypothesis.  Most research projects require the understanding and judicious use of statistical and mathematical tools we will be learning in this course.  While technologies that generate data will continue to evolve, the mathematical and statistical tools available will continue to remain "current."  Understanding and using these is an increasingly important part of a science education.

These mathematical and statistical tools are useful for a broad range of applications, particularly those that involve working with large data sets.  Applications include solving problems such as apportioning the hydrocarbon air pollutants in a region to specific sources, controlling a major industrial chemical process, evaluating the impurities present in a pharmaceutical product, and determining the amount of moisture in wheat from a satellite.  One can even apply these tools to determine the most powerful counting system to use in the game of 21!!

The course will include extensice use of Excel and Matlab software; the latter is available on the Winthrop system from any networked PC on campus.

We will begin by examing the use of matrices to establish regression models.  We will then focus on classification and calibration techniques with significant work involving principle components analysis and some of the various chemometrics techniques that rely upon PCA.

Schedule: Lectures are scheduled at the appointed hour in the assigned classroom.  The course syllabus provides the specific schedule.  All course information is posted on the chemistry department's web page (chem.winthrop.edu).

Class Preparation: Problem sets will be assigned and due at the beginning of the following class.

Graded Exercises: Grades will be primarily based upon problem sets, projects, presentations of work, and final performance-based examination.

Attendance: You are expected to attend all class meetings for the full scheduled time.