Food analysis by portable NIR spectrometer. (October 2022)

• Record Type: Journal Article
• Authors: Folli, Gabriely S.; Santos, Layla P.; Santos, Francine D.; Cunha, Pedro H.P.; Schaffel, Izabela F.; Borghi, Flávia T.; Barros, Iago H.A.S.; Pires, André A.; Ribeiro, Araceli V.F.N.; Romão, Wanderson; Filgueiras, Paulo R.
• Is Part Of: Food Chemistry Advances. Volume 1(2022)
• Publication Date: 2022-10
• Subjects: Tamper -- handheld -- chemometrics -- artificial outliers -- portable NIR -- foods -- on site analysis; 664
• DOI: 10.1016/j.focha.2022.100074
• Languages: English
• ISSNs: 2772-753X

Abstract

Highlights: NIR portable is an efficient technique for food quality control. Adulterants commonly used in EVOO, Honey, yogurt, and milk were identified with NIR portable. SVM provides better results than PLS-DA in identifying adulterants. SVM multi-class provides excellent results food authentication using one-class SVM. All PLS regression results showed linearity for test set above 0.90 and RMSEP below 4.5 wt%. 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%). Graphical Abstract: Image, graphical abstract