Machine learning with insufficient data for classification of mixtures of sunflower and olive oil samples using laser-induced fluorescence spectroscopy

The question of verification of food quality has stood before scientists since ancient times, and, nowadays, the advances in science and technology have made it a very challenging task. Laser-induced fluorescence (LIF) spectroscopy has become a very useful instrument for sample characterization. Nevertheless, analysis of complex multi-component spectra is difficult to approach. In recent years, the capabilities of artificial intelligence have attracted a lot of attention, as they open doors to efficient solutions of many problems that otherwise require a lot of time, effort, expenses and often inspiration. In the present work, we use LIF spectra of mixtures of sunflower and extra virgin olive oils with different concentrations and apply neural network (NN) algorithms with the aim of improving the strategies for concentration determination. Two different approaches have been applied and their output has been compared and commented. More specifically, the task of concentration recognition has been targeted as a classification and as a fitting problem. We formulate four diagnostic parameters with biochemical meaning and compare the NN performance when training with raw spectra and with the diagnostic parameters. The correct choice of appropriate diagnostic parameters is of importance from the point of view of biochemical interpretability and analysis, whereas “black box” full-spectra training might be beneficial for end-user applications. Our results show that these methods perform well even with very scarce data and outline preliminary strategies for defining diagnostic criteria.