Data Fusion in LIBS Food Analysis

Abstract

Recent years have witnessed remarkable advancements in spectral analytical techniques, such as ultraviolet-visible (UV-Vis) spectroscopy, mid-infrared (MIR) spectroscopy, near-infrared (NIR) spectroscopy, Raman spectroscopy, terahertz (THz) spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, and laser-induced breakdown spectroscopy (LIBS). These techniques enable detailed chemical information extraction from spectral data, significantly improving the robustness, precision, and accuracy of analytical results through chemometric and data science methods. The advent of artificial intelligence, big data, and cloud computing has further revitalized chemometric strategies, particularly for spectral analysis of solid samples. Innovations include spectral preprocessing, wavelength selection, data projection in lower dimensions, quantitative calibration, pattern recognition, calibration transfer, and multispectral data fusion. Integrating spectroanalytical techniques with LIBS represents a breakthrough in analytical chemistry. While LIBS excels in rapid elemental analysis with minimal sample preparation, other techniques contribute comprehensive spectral data, including molecular features and trace element information. This synergistic fusion enhances analytical precision, accelerates workflows, and broadens applications across diverse fields. In food analysis and authentication, data fusion models have emerged as pivotal strategies, addressing challenges posed by nontargeted methods and offering deeper insights into complex matrices. Careful assessment of redundancy and synergy among techniques ensures the effective implementation of data fusion, reducing errors and enriching model interpretation.