Optimized ICP-MS Methods for Matrix Interference Control in Food Matrices

Abstract

Inductively coupled plasma–mass spectrometry (ICP-MS) is a high-sensitivity analytical platform with substantial potential for multielement determination in complex food matrices. However, matrix-derived effects and spectral interferences can compromise analytical accuracy, precision, detection capability, and confidence in trace-element measurements. This paper develops a conceptual methodological framework for optimizing ICP-MS procedures for matrix interference control in food analysis. The framework emphasizes matrix characterization, sample preparation, interference identification, instrumental optimization, calibration strategy, internal-standard correction, and analytical quality assessment as interconnected components rather than isolated procedural steps. A structured synthesis of the references supplied by the author was also undertaken. Importantly, the supplied references primarily address postpartum depression and therefore do not provide empirical evidence concerning ICP-MS, food matrices, spectral interference, or elemental analysis. Consequently, no unsupported ICP-MS findings have been attributed to those sources. Instead, the methodological model is presented as a technically reasoned research framework requiring experimental validation. The proposed approach distinguishes spectral interference from nonspectral matrix effects and emphasizes adaptive optimization according to food composition and target analytes. The analysis indicates that reliable ICP-MS food analysis depends not merely on instrument sensitivity but on coordinated control of sample-derived ionization effects, polyatomic species, calibration behavior, and analytical verification. The framework provides a basis for future experimental studies comparing interference-reduction strategies across representative food matrices.