Partial least squares discrimination applied to a few samples dataset: A case for predicting the presence of pesticide in lettuce. (28th August 2020) - de Souza, Silvio José; Valderrama, Patrícia; Consolin Filho, Nelson; Pilau, Eduardo Jorge; Coelho Tanamati, Ailey Aparecida; Wentzell, Peter D.; Março, Paulo Henrique
Abstract: To perform discriminant analysis through partial least squares (PLS‐DA), it is important to note that the smaller the set of samples and the higher the variable to sample ratio, the higher the chance of a too optimistic classification model. In this sense, it is necessary to use strategies to check for the possibility that the classification achieved is done by chance. In metabolomics studies, it is not uncommon to work with a reduced number of samples, in which discrimination approaches must be evaluated according to its reliability. Considering this issue, this study aimed to show a case study to deal with few samples using PLS‐DA to discriminate lettuce cultivated in the absence and presence of imidacloprid (IMI). The data were acquired by using ultra‐high‐performance liquid chromatography coupled to a quadrupole‐time of flight mass spectrometry, and the model prediction ability was evaluated by permuting the classes. The performance of the PLS‐DA model built using all the variables reached 100% correct classification. Nonetheless, the reliability tests (Wilcoxon, sign test, and Rand t test) indicated that the model has been build choosing variables by chance. By using the variable importance in projection, it was possible to build a model with reliable specificity and sensitivity equals 1. The study showed the need to check the classification ability in PLS‐DA models through strategies such as variable selection and the permutation test in order to allow for the evaluation of the reliability of the results, even in cases in which the classification reaches 100% in the target.