Portable optical spectroscopy and machine learning techniques for quantification of the biochemical content of raw food materials

Accuracy in determining food authenticity, possible contamination, content analysis, and even geographical origin is of considerable scientific and economic value. The aim of this study is to facilitate quantitative evaluation of protein content in the seeds of cereals (Triticum turgidum var. durum and Tritordeum genotypes) and ripening pomegranate fruits (Wonderful cultivar). Two species of wheat were evaluated in this study: durum wheat, Triticum turgidum var. durum, and Tritordeum (durum wheat × wild barley) together with pomegranate fruits of the variety Wonderful. Two different portable Near InfraRed (NIR) spectrometers have been used: a prototype developed in the PhasmaFood project and the commercial SCiO™ molecular sensor. Considering the specific samples, the obtained results of the classification models indicate a validation mean absolute error of 0.8