Stable isotope profiling and machine learning for determining cocoa origin, ingredient composition, and cocoa content
Karina Gonçalves da Silva1, Marcelo Zacharias Moreira1, Antonio Vargas de Oliveira Figueira2
1Laboratory of Isotope Ecology, Center for Nuclear Energy in Agriculture, University of São Paulo, Av. Centenário, 303, São Dimas, Piracicaba CEP 13416-000, SP, Brazil.
None:
Chocolate is a versatile product with flavours ranging from sweet to bitter. Cocoa, from a C3-photosynthetic plant, is its main raw material, while sugar from sugarcane (C4-metabolism) is commonly used. We 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. 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. These findings demonstrate that stable isotope analysis is a rapid, secure, and effective tool for determining composition, origin, and authenticity in chocolate.
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