Measuring trace element fingerprinting for cereal bar authentication based on type and principal ingredient

Michael Pérez-Rodríguez; Melisa Jazmin Hidalgo; Alberto Mendoza; Lucy T González; Francisco Longoria Rodríguez; Héctor Casimiro Goicoechea; Roberto Gerardo Pellerano

doi:10.1016/j.fochx.2023.100744

Measuring trace element fingerprinting for cereal bar authentication based on type and principal ingredient

Food Chem X. 2023 Jun 7:18:100744. doi: 10.1016/j.fochx.2023.100744. eCollection 2023 Jun 30.

Authors

Michael Pérez-Rodríguez¹, Melisa Jazmin Hidalgo², Alberto Mendoza¹, Lucy T González¹, Francisco Longoria Rodríguez³, Héctor Casimiro Goicoechea⁴, Roberto Gerardo Pellerano²

Affiliations

¹ Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey 64849, N.L., Mexico.
² Instituto de Química Básica y Aplicada del Nordeste Argentino (IQUIBA-NEA), UNNE-CONICET, Facultad de Ciencias Exactas y Naturales y Agrimensura, Ave. Libertad 5400, Corrientes 3400, Argentina.
³ Centro de Investigación en Materiales Avanzados, S. C. (CIMAV), Unidad Monterrey, Alianza Norte 202, Apodaca 66628, N.L., Mexico.
⁴ Laboratorio de Desarrollo Analítico y Quimiometría (LADAQ), Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral, Ciudad Universitaria, Santa Fe 3000, Argentina.

Abstract

This paper introduces a method for determining the authenticity of commercial cereal bars based on trace element fingerprints. In this regard, 120 cereal bars were prepared using microwave-assisted acid digestion and the concentrations of Al, Ba, Bi, Cd, Co, Cr, Cu, Fe, Li, Mn, Mo, Ni, Pb, Rb, Se, Sn, Sr, V, and Zn were later measured by ICP-MS. Results confirmed the suitability of the analyzed samples for human consumption. Multielemental data underwent autoscaling preprocessing for then applying PCA, CART, and LDA to input data set. LDA model accomplished the highest classification modeling performance with a success rate of 92%, making it the suitable model for reliable cereal bar prediction. The proposed method demonstrates the potential of trace element fingerprints in distinguishing cereal bar samples according to their type (conventional and gluten-free) and principal ingredient (fruit, yogurt, chocolate), thereby contributing to global efforts for food authentication.

Keywords: Authentication; Cereal bars; Fingerprinting; ICP-MS; LDA; Trace elements.