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data preprocessing (2)

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.

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Researchers at BfR (the German Federal Institute of Risk Assessment) have developed a non-targeted method to detect 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. The performances of the 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 preprocessing methods including standard normal variate (SNV), first and second derivatives, smoothing, and combinations thereof, it was possible to identify the adulterants with a specificity of greater than 80% .

Read the abstract at: paprika adulteration

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