German researchers have developed a method for the non-targeted detection of paprika adulteration using Fourier transform mid-infrared (FT-MIR) spectroscopy and one-class soft independent modelling of class analogy (OCSIMCA). One-class models based on commercially available paprika powders were developed, and optimised to provide a sensitivity greater than 80% by external validation. These models for adulteration detection were tested by predicting spiked paprika samples with various types of fraudulent material and levels of adulterations including 1% (w/w) Sudan I, 1% (w/w) Sudan IV, 3% (w/w) lead chromate, 3% (w/w) lead oxide, 5% (w/w) silicon dioxide, 10% (w/w) polyvinyl chloride, and 10% (w/w) gum arabic. By applying different data preprocessing chemometric methods, a classification specificity greater than 80% was achieved for all adulterants.
Read the abstract here