Chemometric studies of some trace elements in cows' milk

Area of Science:

• Analytical Chemistry
• Food Science
• Geochemistry

Background:

• Trace elements in milk are crucial indicators of animal health and environmental factors.
• Understanding trace element composition can reveal geographical and agricultural influences on milk quality.
• Principal component analysis (PCA) is a powerful statistical tool for reducing data dimensionality and identifying patterns.

Purpose of the Study:

• To analyze the correlation matrix of 11 trace elements in cow's milk using PCA.
• To identify clusters of trace elements based on their concentrations.
• To assess the utility of PCA in differentiating Italian milk from various geographical origins.

Main Methods:

• Correlation matrix of 11 trace elements from 45 cow's milk samples.
• Application of principal component analysis (PCA) to reduce data dimensionality.
• Orthogonal rotation of principal components to interpret factors.
• Plotting principal component scores for geographical differentiation.

Main Results:

• PCA reduced 11 variables to 5 principal components, explaining approximately 80% of the total variance.
• Specific factors showed distinct correlations with groups of elements (e.g., F1 with Cr, Mn, Fe; F3 with Cd, Pb).
• Element clusters were observed, suggesting common origins or influences.
• PCA successfully differentiated Italian milk samples based on their geographical origin.

Conclusions:

• Trace element composition in cow's milk is influenced by geographical origin.
• PCA is an effective method for identifying trace element clusters and their potential origins.
• PCA-based analysis of milk trace elements can serve as a tool for geographical origin discrimination.