During a short chemometrics course
in the seventh semester of the
chemistry undergraduate program, students receive a brief theoretical
introduction to multivariate calibration, focused on partial least-squares
regression as the most commonly employed data processing tool. The
theory is complemented with the use of MVC1_R, an easy-to-use software
developed in-house as an R Shiny application. The present report describes
student activities with the latter software in the development of
mathematical models to predict quality parameters of corn seeds from
near-infrared spectra. Subsequently, an experimental project is carried
out involving near-infrared spectral measurements, which are widely
used in several industrial fields for quality control. To process
the obtained data, students apply the knowledge acquired during the
theoretical/software sessions.