The aim of this manuscript was to show the concepts involving data treatment of RGB based image analysis by univariate and multivariate approaches - the latter using Partial Least Squares (PLS) - using as a practical example application the determination of extravirgin olive oil adulteration. Digital images were collected using a device made of LEDs, batteries and a smartphone and results are shown in a tutorial format using Matlab computing environment. The experiment can serve as an example for teaching the subject for undergraduate and graduate students.

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.

The objective of this manuscript is to describe a practical experiment that can be employed for teaching concepts related to design of experiments using Matlab or Octave computing environment to beginners, undergraduate and graduate students. The classical experiment for determination of Fe (II) using o-phenanthroline was selected because it is easy to understand, and all the required materials are readily available in most analytical laboratories. The approach used in this tutorial is divided in two steps: first, the students are introduced to the concept of multivariate effects, how to calculate and interpret them, and the construction and evaluation of a linear model to describe the experimental domain by using a 2³ factorial design. Second, an extension of the factorial design by adding axial points is described, thereby, providing a central composite design. The quadratic model is then introduced and used to build the response surface.

The aim of this manuscript was to show the basic concepts and practical application of Partial Least Squares (PLS) as a tutorial, using the Matlab computing environment for beginners, undergraduate and graduate students. As a practical example, the determination of the drug paracetamol in commercial tablets using Near-Infrared (NIR) spectroscopy and Partial Least Squares (PLS) regression was shown, an experiment that has been successfully carried out at the Chemical Institute of Campinas State University for chemistry undergraduate course students to introduce the basic concepts of multivariate calibration in a practical way.

Raman imaging spectroscopy is a highly useful analytical tool that provides spatial and spectral information on a sample. However, CCD detectors used in dispersive instruments present the drawback of being sensitive to cosmic rays, giving rise to spikes in Raman spectra. Spikes influence variance structures and must be removed prior to the use of multivariate techniques. A new algorithm for correction of spikes in Raman imaging was developed using an approach based on comparison of nearest neighbor pixels. The algorithm showed characteristics including simplicity, rapidity, selectivity and high quality in spike removal from hyperspectral images.

This manuscript aims to show the basic concepts and practical application of Principal Component Analysis (PCA) as a tutorial, using Matlab or Octave computing environment for beginners, undergraduate and graduate students. As a practical example it is shown the exploratory analysis of edible vegetable oils by mid infrared spectroscopy.