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

Area of Science:

• Analytical Chemistry
• Food Science
• Chemometrics

Background:

• Food authenticity is crucial for consumer safety and regulatory compliance.
• Trace element analysis offers a promising approach for food fingerprinting.
• Existing methods may lack the specificity to differentiate complex food matrices like cereal bars.

Purpose of the Study:

• To develop and validate a novel method for authenticating commercial cereal bars.
• To investigate the potential of trace element profiles for classifying cereal bars by type and ingredient.
• To establish a reliable predictive model for cereal bar authentication.

Main Methods:

• Sample preparation involved microwave-assisted acid digestion of 120 cereal bars.
• Elemental concentrations (Al, Ba, Bi, Cd, Co, Cr, Cu, Fe, Li, Mn, Mo, Ni, Pb, Rb, Se, Sn, Sr, V, Zn) were measured using ICP-MS.
• Multielemental data were preprocessed (autoscaling) and analyzed using Principal Component Analysis (PCA), Classification and Regression Trees (CART), and Linear Discriminant Analysis (LDA).

Main Results:

• All analyzed cereal bar samples were confirmed to be suitable for human consumption.
• The Linear Discriminant Analysis (LDA) model demonstrated the highest classification performance, achieving a 92% success rate.
• Trace element fingerprints effectively distinguished cereal bar samples based on type (conventional vs. gluten-free) and principal ingredient (fruit, yogurt, chocolate).

Conclusions:

• Trace element analysis, coupled with chemometric methods like LDA, provides a robust and accurate approach for cereal bar authentication.
• The developed method can reliably differentiate cereal bars by their formulation, aiding in quality control and combating food fraud.
• This technique contributes to global food authentication efforts by offering a sensitive and specific analytical tool.