Food analysis by portable NIR spectrometer
Gabriely S. Folli, Layla P. Santos, Francine D. Santos, Pedro Henrique Pereira da Cunha, Izabela de França Schaffel, Flávia T. Borghi, Iago H.A.S. Barros, André Assis Pires, Araceli Verónica Flores Nardy Ribeiro, Wanderson Romão, Paulo R. Filgueiras
Abstract
Extra-virgin-olive oil, honey, milk, and yogurt have associated high nutritional and commercial value. Tampering/non-conformance in these products can damage consumer's health. Therefore, rigorous quality control over the ingredients purity and declaration is necessary. The Near-infrared (NIR) is used to identify/quantify food adulterants, however the developed analytical methodologies need multivariate analysis. The portable NIR instrument enables on-site analysis, requires a few seconds, small sample volume, no sample destruction, and presents low maintenance costs. In this paper we were to classify [one-class and multi-class Support Vectors Machine (SVM), Partial Least Squares Discriminant Analysis (PLS-DA)] and PLS to quantify food adulterants using a portable NIR. The generation of artificial outliers in the one-class SVM models showed satisfactory results for authenticity analysis. The results showed that SVM (Test accuracy = 0.90-1.00) obtained better metrics compared to PLS-DA (Test accuracy = 0.83-0.97). The PLS obtained excellent accuracy: honey (RMSEP = 0.57 wt%), EVOO (RMSEP = 2.06 wt%), milk (RMSEP = 0.20 wt%), and yogurt (RMSEP = 0.06 wt%).