Although analytical techniques to assess the degradation of transformer insulation systems are well established, it is essential to develop fast and effective methodologies for monitoring the quality of power transformer insulating fluid. The objective of this work was to develop a fast, simple, and environmentally friendly method to evaluate and monitor the quality of insulating vegetable oil (IVO) from transformers, using multivariate control chart and infrared spectroscopy (FTIR). For the development of the method, IVO samples were subjected to electrical and thermal stress, as well as to contact with insulating paper and copper, to simulate the internal environment of a transformer before and after energizing. Control chart based on principal component analysis was developed to monitor insulating vegetable oil quality. It was possible to identify oil samples with oxidation levels that were outside the acceptable quality standards for insulating fluid in transformers. The results demonstrated the potential of FTIR spectroscopy and control charts as fast and simple tools for monitoring the quality of insulating fluids, enabling the identification of oil samples that fail to comply with regulation standards for insulating fluids, assisting in the implementation of maintenance programs for power transformers.

Analytical Chemistry disciplines are present in the curriculum of several undergraduate courses. However, based on our experience, students not always are able to realize the association of the scientific concepts included in these disciplines with the social context and, specially, with their future field of work. In the present report, we used a case study about calcium supplementation by calcium carbonate and calcium chelate that included a science outreach activity, which was applied to students enrolled in three undergraduate courses: Pharmacy, Biomedicine, and Chemistry. We noticed that students not only got actively involved with the case, but also highlighted the importance of this activity, took the case beyond the classroom, and requested the inclusion of other similar activities in the discipline. Although different answers were presented for the case, the results obtained bring a positive impact on the instructors’ teaching practice and on the courses as a whole.

A teaching experiment on supervised pattern recognition in chemometrics was proposed in this tutorial to introduce partial least squares discriminant analysis (PLS-DA). A new approach of the experiment published in the first tutorial of this series was revisited and employed to the classification of edible vegetable oils. The spectra of olive, canola, soybean and corn oils were obtained using an attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectrometer in the range of 600 to 4000 cm-1. The combination of ATR-FTIR and PLS-DA classification method was able to correctly classify 100% of the validation samples. The Matlab commands, routines and functions were presented, and a didactic explanation of the concepts and interpretation of the data was provided.