AI Article Synopsis
- The study explored using near-infrared (NIR) and mid-infrared (MIR) spectroscopy combined with chemometrics to verify the authenticity of Iranian saffron and detect potential adulteration.
- The research found that NIR spectroscopy was more effective than MIR in identifying the origin of saffron samples, using methods like principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA).
- Moreover, PLSR analysis demonstrated that NIR outperformed MIR in estimating the level of adulteration, achieving high regression coefficients between 0.95 and 0.99.
Article 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) in range of 0.95-0.99.
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| http://dx.doi.org/10.1016/j.foodchem.2020.128647 | DOI Listing |
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