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This study (purchase required) shows the potential for stable isotope ratio analysis (SIRA) to be used to verify whether the cocoa in bean-to-bar chocolate originated from the Amazon region.  Higher-sugar chocolate is too complex a product to test in this way.  Such tests will be important for checking compliance with future EU Deforestation Regulations. The proposed method can also be used to estimate the cocoa content of chocolate.

Chocolate is a complex product in terms of its carbon isotope distribution.  Cocoa, from a C₃-photosynthetic plant, is its main raw material, while sugar from sugarcane (C₄-metabolism) is also commonly included. The authors analyzed the δ¹³C and δ¹⁵N composition of Brazilian chocolates, including Conventional, Bean-to-bar, and Imported brands across White, Milk, Semisweet, Dark <70 %, and Dark ≥70 % versions. They report that conventional White, Milk, and Semisweet chocolates showed no significant isotopic differences, with average δ¹³C values around −22 ‰, indicating high C₄-derived ingredient content. Bean-to-bar chocolates presented δ¹³C values 2–3 ‰ lower, and those made with Amazon cocoa were ∼1.5 ‰ lighter than those from Atlantic Forest, enabling accurate prediction of cocoa origin. Imported chocolates showed even lighter δ¹³C values, suggesting greater use of C₃-based ingredients. δ¹³C and δ¹⁵N values also enabled reliable estimation of cocoa content.

Photo by Boudhayan Bardhan 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

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.

This paper (purchase required) reports the use of direct analysis in real time mass spectrometry (DART-MS) as a rapid laboratory-based authentication test with the potential for a portable device. Reference samples of cocoa powders, carob flours, and mixtures of the two were extracted with buffer and interrogated by DART-MS. The mass spectra were used to develop classification models. A principal component-linear discriminant analysis (PCA-LDA) model was used to discriminate between cocoa powder and cocoa powder amended with 15% carob flour. The accuracy using internal validation was 100%. Using an external validation dataset, the accuracy, precision, and recall were 96.0%, 94.8%, and 97.3%, respectively.

For a descriptor of DART-MS see FAN’s analytical method explainers.