Advanced multimodeling of isotopic and elemental content for cheese authentication

Area of Science:

• Food Science
• Analytical Chemistry
• Chemometrics

Background:

• Dairy product authentication is crucial for food fraud prevention, traceability, and consumer safety.
• Cheese composition, influenced by environment and processing, allows for origin and type differentiation.
• Elemental and isotopic analysis provides a basis for distinguishing cheese samples.

Purpose of the Study:

• To develop and evaluate chemometric models for differentiating cheese samples based on geographical origin and type.
• To assess the interaction effects between geographical origin and cheese type.
• To identify key elemental and isotopic markers for cheese authentication.

Main Methods:

• Utilized linear discriminant analysis (LDA) and generalized linear models (GLMs).
• Analyzed elemental (Mg, K, Ca, Cr, Mn, Co, Ni, Cu, Zn, As, Cd, Pb) and isotopic (δ¹³C_cheese, δ¹³C_casein) compositions.
• Applied models to discriminate cheese samples by origin and type, and evaluate interaction effects.

Main Results:

• LDA identified Mn, Zn, Co, Ca, Cr, and δ¹³C_cheese as key predictors for geographical origin differentiation (82.2% initial classification).
• Cu, Zn, Mg, and δ¹³C_casein were significant for differentiating cheese varieties.
• GLMs were developed to assess main and interaction effects of origin and type for various elements and isotopes.

Conclusions:

• LDA and GLMs are suitable chemometric tools for developing robust cheese differentiation models.
• Elemental and isotopic analyses effectively distinguish cheese samples by geographical origin and type.
• The study successfully evaluated the interaction between geographical origin and cheese type using these analytical methods.