Classification of cereal bars using near infrared spectroscopy and linear discriminant analysis

This work proposes an analytical method for cereal bar classification based on the use of near infrared spectroscopy (NIRS) and supervised pattern recognition techniques. Linear discriminant analysis (LDA) is employed to build a classification model on the basis of a reduced subset of variables (wavenumbers). For the purpose of variable selection, three techniques are considered, namely successive projection algorithm (SPA), Genetic Algorithm (GA), and stepwise (SW) formulation. The methodology is validated in a case study involving the classification of 121 cereal bar samples into three different types (conventional, diet and light). The results show that the LDA/GA model is superior to the LDA/SPA and LDA/SW models with respect to classification accuracy in an independent prediction set. Some advantages of the proposed method are speed, that the analytical measurement is performed quickly (one minute or less per sample), no reagents, low sample consumption and minimum sample preparation demands. In view of the results obtained in this study the proposed method may be considered valid for use in cereal bar classification.

► Method based on near infrared spectroscopy to perform classification of cereal bars ► Linear discriminant analysis employed with variable selection using SPA, GA and SW ► Wavenumber selection is key to an accurate discrimination of the samples.