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This study (purchase required) reports a direct method to verify the purity and authenticity of commercial sweetener raw materials; erythritol, xylitol, and stevia.  Analysis is by near- and mid-infrared spectroscopy combined with a DD-SIMCA classification model. The model was enhanced with virtual samples created by adding PCA residuals and noise.  The authors report that this improved the model's robustness and accuracy. Validation was performed using independent sample sets, including commercial natural sweeteners and in-house samples adulterated with saccharin, sucrose, acesulfame, and silicon dioxide.

The authors conclude that the approach was efficient for xylitol and erythritol authentication.  Efficiency rates were 90 % or higher for xylitol, erythritol, and stevia, but stevia sampling is challenging due to stevia's variable composition and needs improvement before the model could be applied with confidence.

Photo by rama purnama on Unsplash

Cocoa is high on many companies’ current risk radar for authenticity threats, due to recent supply pressures and price increases. Carob has legitimate uses as a cocoa replacement, and carob flour has been cited as a potential cocoa adulterant.

 A number of chemometric classification methods to differentiate cocoa from carob recently have been proposed, including one featured in our blogs in January based on DART-MS.  In a more recent publication (purchase required) the authors use the alternate method of near and mid-infrared spectroscopy before applying various chemometric approaches.

Spectral data were collected using four different infrared spectrometers: a benchtop FT-NIR system, two portable NIR instruments, and a benchtop FT-MIR-ATR. Reference samples included pure cocoa, pure carob, and their mixtures with carob concentrations ranging from 0 % to 60 %. Both classification and regression models were developed to detect and quantify the presence of carobs in cocoa powder. Classification models, including Random Forest (RF), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), k-Nearest Neighbors (kNN), Linear Discriminant Analysis (LDA), and Soft Voting Classifiers, demonstrated superior performance for discriminating between cocoa powder, carob powder, and cocoa-carob mixtures, particularly using the benchtop FT-NIR. Similarly, regression models - RF, SVM, MLP, kNN, Partial Least Squares Regression, and Voting Regressor- exhibited robust predictive capabilities, particularly, FT-MIR and portable NIR.

Overall, these findings highlight and prove the potential of NIR and MIR spectroscopy as rapid, robust, and non-destructive tools for screening and quality control in food authentication.

Photo by lindsay Cotter on Unsplash