Comprehensive chemometric classification of snack products based on their near infrared spectra

The authentication and quality assurance of snack products have become important, since these convenience foods are popular in the modern lifestyle. Near infrared spectroscopy with machine learning algorithms can be used for the determination of qualitative properties of these products. Our study focuses on the determination of four important qualitative aspects of snacks: a) frying oil, b) raw material, c) place of origin, d) production technology. Our aim was to classify snack samples from various countries and producers successfully based on the aforementioned criteria. Three well-known machine learning algorithms, namely partial least squares discriminant analysis (PLS-DA), multilayer feed-forward of resilient backpropagation network (RPropMLP) and random forest (RF) were applied for the task, with custom validation protocols, optimized for the number of samples. Cross-validation and test validation were used to verify the robustness of the models. Accuracy of the models was above 0.80 in each case. The results showed that the neural network-based algorithm outperformed the other algorithms in every case based on the accuracy and area under the ROC curve values, which reveals a potential advantage of neural network-based algorithms in the case of smaller datasets. The models can be easily implemented to the quality control of snack products. •Chemometric analysis of 155 commercial savory snack products measured by FT-NIR.•Classification by PLS-DA, Random forest and a neural network-based algorithm.•Classification of frying oil, raw material, place of origin, production technology.•Frying oil detection proved to be the hardest task for the algorithms.•The neural network-based algorithm outperformed the PLS-DA and Random forest models.