Pérez-Rodríguez Michael, Jazmin Hidalgo Melisa, Mendoza Alberto, González Lucy T, Longoria Rodríguez Francisco, Casimiro Goicoechea Héctor, Gerardo Pellerano Roberto
Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey 64849, N.L., Mexico.
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.
Food Chem X. 2023 Jun 7;18:100744. doi: 10.1016/j.fochx.2023.100744. eCollection 2023 Jun 30.
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.
本文介绍了一种基于微量元素指纹图谱来确定市售谷物棒真伪的方法。在此方面，采用微波辅助酸消解制备了120个谷物棒样品，随后通过电感耦合等离子体质谱法（ICP-MS）测定了其中铝（Al）、钡（Ba）、铋（Bi）、镉（Cd）、钴（Co）、铬（Cr）、铜（Cu）、铁（Fe）、锂（Li）、锰（Mn）、钼（Mo）、镍（Ni）、铅（Pb）、铷（Rb）、硒（Se）、锡（Sn）、锶（Sr）、钒（V）和锌（Zn）的浓度。结果证实了所分析的样品适合人类食用。对多元素数据进行了自动缩放预处理，然后将主成分分析（PCA）、分类与回归树（CART）和线性判别分析（LDA）应用于输入数据集。LDA模型实现了最高的分类建模性能，成功率为92%，使其成为可靠的谷物棒预测的合适模型。所提出的方法证明了微量元素指纹图谱在根据谷物棒的类型（传统型和无麸质型）和主要成分（水果、酸奶、巧克力）区分谷物棒样品方面的潜力，从而为全球食品认证工作做出贡献。
