ft-nir (5)

 

8169733900?profile=RESIZE_400xItalian researchers have developed a method based on FT-NIR (Fourier transform - near infrared) spectroscopy combined with chemometrics to authenticate pasta made exclusively with durum wheat. In addition, the objective of this study was to verify that the pasta was made with 100% Italian durum wheat. The 361 samples used were pasta marketed in Italy and made with durum wheat cultivated in Italy (n = 176 samples), and on pasta made with mixtures of wheat cultivated in Italy and/or abroad (n = 185 samples). The samples were analysed by FT-NIR spectroscopy coupled with supervised classification models. Good performance results of the validation set (sensitivity of 95%, specificity and accuracy of 94%) were obtained using principal component-linear discriminant analysis (PC-LDA), which clearly demonstrated the high prediction capability of this method. 

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3925480647?profile=RESIZE_400xFaster screening methods are becoming increasingly important to permit rapid analyses, and FT-NIR spectroscopy offers the possibility of such a method.  Validation of screening methods requiring the application of multivariate data treatment is less frequently described in literature, thus limiting their use as routine tools in control laboratories for food fraud monitoring. Guidelines for validating screening methods for EU Official Control Laboratories involving pesticide residues, GMOs, allergens mycotoxins etc., were issued in 2013. In this research, an  EU-validation procedure for screening methods was developed and successfully applied to a multivariate FT-NIR spectroscopic method for the screening of durum wheat pasta samples adulterated with common wheat at the screening target concentration of 3%.  The results obtained demonstrated that the validation approach would be a proof-of-strategy to be used for multivariate infrared methods applied for screening purposes. 

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4439648992?profile=RESIZE_400xQuantitative 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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Because there is such a high worldwide demand for cinnamon spices, true cinnamon (Cinnamomum verum) powder is often adulterated with another inferior quality of cinnamon known as cassia cinnamon (Cinnamomum aromaticum). Korean researchers employed Fourier transform near-infrared (FT-NIR) and Fourier transform infrared (FT-IR) spectroscopic analysis to determine the spectral differences in authentic and adulterated samples. Absorbance spectra of 195 samples of true, cassia and various adulterated samples (5-50% w/w adulterant) with 15 replicates for each sample  were collected. The partial least square regression (PLSR) models with spectral pre-processing methods were applied to predict the presence of cassia cinnamon in true cinnamon powder. The predictive value of FT-NIR data was greater than the FT-IR data. The study shows that FT-NIR and FT-IR spectroscopic techniques combined with multivariate analysis could be utilised as a controlled procedure or as an alternative rapid detection method to identify adulterated cinnamon powder.

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