Chemometrics for Quantitative Determination of Terpenes Using Attenuated Total Reflectance–Fourier Transform Infrared Spectroscopy: A Pedagogical Laboratory Exercise for Undergraduate Instrumental Analysis Students

Abstract

Chemometric techniques such as partial least-squares (PLS) regression have been applied with huge success to a wide array of chemical problems including multicomponent analysis of analytes in complex mixtures. Despite this, there are few examples of laboratory pedagogical exercises that involve students in the acquisition of chemical data from infrared spectroscopicbased instrumentation, followed by quantitative chemometric analysis using PLS. In this article, we present a computational activity that introduces undergraduate students in an instrumental analysis laboratory setting to data acquisition using attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy followed by data analysis using PLS. The first part of the activity involved students creating mixture designs of binary terpene components consisting of pcymene and limonene. These mixtures were then analyzed using an ATR-FTIR spectrometer, where students became familiarized with the instrument and were shown how to characterize and differentiate the aforementioned terpenes using their generated FTIR spectra. The second part of the activity involved preprocessing the acquired FTIR spectral data from the first part followed by the simultaneous quantitative determination of the prepared terpenes using PLS. On the basis of student surveys, it can be concluded that this convenient and inexpensive activity was ultimately successful in introducing students to the use of chemometrics for quantitative analysis of terpenes using an ATR-FTIR. This easy-to-do two-week activity can be used as a standalone for instrumental analysis laboratory classes and can even be integrated in advanced courses in applied spectroscopy and chemometrics.

Teaching Descriptive Statistics and Hypothesis Tests Measuring Water Density

“…As an alternative, programming software such as MATLAB, 32−40 Python, 41−43 Octave, 44−47 R project. 48 had been used. However, students were often unfamiliar with those programs.…”

“…Descriptive statistics and parametric hypothesis tests ( t test and one-way ANOVA − ) were taught using spreadsheets. − However, spreadsheets did not provide nonparametric tests, and a visual interpretation of these tests was not a default option. As an alternative, programming software such as MATLAB, − Python, − Octave, − R project . had been used.…”

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