Comparison of near-infrared (NIR) and mid-infrared (MIR) spectroscopy based on chemometrics for saffron authentication and adulteration detection. (15th May 2021)

• Record Type: Journal Article
• Authors: Amirvaresi, Arian; Nikounezhad, Nastaran; Amirahmadi, Maryam; Daraei, Bahram; Parastar, Hadi
• Is Part Of: Food chemistry. Volume 344(2021)
• Publication Date: 2021-05-15
• Subjects: Chemometrics -- Saffron -- Adulteration -- Near-infrared spectroscopy -- Mid-infrared spectroscopy -- Authentication; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2020.128647
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: Integration of NIR/MIR with chemometrics is proposed for saffron authentication. PCA is used for clustering of saffron samples based on geographical origin. PLS-DA is used for classification of saffron and plant-derived adulterated samples. PLSR is utilized for estimation of adulteration amount in adulterated samples. NIR outperformed over MIR for authentication and adulteration detection. Abstract: In this work, the potential of near-infrared (NIR) and mid-infrared (MIR) spectroscopy along with chemometrics was investigated for authentication and adulteration detection of Iranian saffron samples. First, authentication of one-hundred saffron samples was examined by principal component analysis (PCA). The results showed the NIR spectroscopy can better predict the origin of samples than the MIR. Next, partial least squares-discriminant analysis (PLS-DA) was developed to detect four common plant-derived adulterants (i.e., saffron style, calendula, safflower, and rubia). In all cases, PLS-DA classification figures of merit in terms of sensitivity, specificity, error rate and accuracy were satisfactory for both NIR and MIR datasets. The built models were then successfully validated using test set and also commercial samples. Finally, partial least squares regression (PLSR) was used to estimate the amount of adulteration. In this case, only NIR showed a good performance with regression coefficients (R 2 ) in range of 0.95–0.99.