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This study compared the capabilities of three spectroscopic techniques as fast screening platforms for honey authentication purposes. Multifloral honeys were collected in the three main honey-producing regions of Argentina over four harvesting seasons to give a total of 502 samples. Spectra were run on each of the samples with FT-MIR ( Fourier transform mid-infrared), NIR (near infrared) and FT-Raman  (Fourier transform Raman)  spectroscopy. The spectroscopic platforms were compared on the basis of the classification performance achieved under a supervised chemometric approach. Very good classification scores to distinguish the three Argentian regions were achieved by all the spectroscopies, and a nearly perfect classification was provided by FT-MIR. The results obtained in the present work suggested that FT-MIR had the best potential for fingerprinting-based honey authentication, and demonstrated that sufficient accuracy levels to be commercially useful can be reached.

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Quantitative DNA methods are used to detect and measure common wheat adulteration of durum wheat pasta. Italian and Argentinian researchers have validated a method for common wheat adulteration using Fourier transformed infrared spectroscopy (FT-IR) and chemomentrics. The dataset used to calibrate this infrared method was from 300 samples of both Italian and Argentinian durum wheat pasta analysed by an ELISA (enzyme-linked immunosorbent assay) method with common wheat adulteration ranging from less than 0.5% to 28%. These samples were analysed by both near- and mid-infrared spectroscopy (FT-NIR, FT-MIR) and the performance results were compared. The spectra were then analysed by two chemometric methods  - Partial-Least Squares Discriminant Analysis (PLS-DA) and Linear Discriminant Analysis (LDA). The first LDA and PLS-DA models grouped samples into three-classes, i.e. common wheat ≤1%, from 1 to ≤5% and >5%; while the second LDA and PLS-DA models grouped samples into two-classes using a cut-off of 2% common wheat adulteration. The accuracy of the validated models were between 80 and 95% for the three-classes approach, and between 91 and 97% for the two-classes approach. The three-classes approach provided better results in the FT-NIR range, while the two-classes approach provided comparable results in both spectral ranges. These results indicate the method could provide a rapid and inexpensive way of determining common what adulteration in durum wheat pasta.

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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