1H NMR spectroscopy, one-class classification and outlier diagnosis: A powerful combination for adulteration detection in paprika powder

• Published in: Food control 2021-10, Vol.128, p.108205, Article 108205
• Main Authors: Horn, Bettina, Esslinger, Susanne, Fauhl-Hassek, Carsten, Riedl, Janet
• Format: Article
• Language: English
• Subjects: DD-SIMCA; Food authenticity; Non-targeted analysis; OPLS; Outlier detection; ROBPCA
• ISSN: 0956-7135; 1873-7129
• DOI: 10.1016/j.foodcont.2021.108205

Paprika powder is a widely consumed high-priced product and therefore particularly prone to fraudulent practices. Nuclear magnetic resonance spectroscopy combined with one-class classification was applied for non-targeted detection of paprika adulteration and further chemometric tools were tested for diagnostic purposes. The 1H NMR spectra of 186 commercial paprika powders and 216 spiked samples were used to develop and comprehensively validate a data-driven soft independent modelling of class analogy model. The established one-class model yielded 92% sensitivity and exemplary adulterants were detected with 100% specificity at concentration levels of 0.1%, 0.1%, 10% and 20% by weight for Azorubine, Ponceau 4R, beetroot and sumac powder, respectively. After successful classification, visualization tools of robust principal component analysis and orthogonal partial least squares analysis were explored to uncover fingerprints of unusual (atypical and spiked) paprika powders. One-class classifiers based on 1H NMR spectroscopic data seem to be suitable for adulteration screening and, in combination with an outlier diagnosis, may improve prioritization of suspicious products for further confirmatory analysis in food authentication. •Approach for authentication of unknown paprika samples and adulteration screening.•Non-targeted approach by combining 1H NMR spectroscopy and one-class classification.•Detection of exemplary colouring and bulking adulterants by a SIMCA model.•Chemometric visualization tools for diagnostic inspection of atypical fingerprints.•Robust principal component analysis for data exploration and outlier diagnosis.