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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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Urea is added as an adulterant to give milk whiteness and increase its consistency by improving the non-fat solids content, but excessive amounts of urea in milk causes overload and kidney damage. A sensitive method for detecting and quantifying urea adulteration of milk has been developed using FT-NIRS (Fourier Transformed Near Infra Red Spectroscopy) coupled with multivariate analysis. The model was developed using 162 fresh milk samples, consisting of 20 non-adulterated samples (without urea), and 142 samples with the urea adulterant at 8 different concentrations (0.10%, 0.30%, 0.50%, 0.70%, 0.90%, 1.10%, 1.30%, and 1.70%), each prepared in triplicate. The NIR data coupled with the PLS‐DA (Partial Least Squares -Discriminant Analysis) model can be used to discriminate between the unadulterated fresh milk samples and those adulterated with urea. Furthermore, the NIR data coupled with PLSR (Partial Least Squares Regression) models may be used to quantify the level of the urea in milk samples.
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