Spectroscopy-multimodal data fusion empowers smart food quality analysis: Challenges and prospects

Description

Zhanming Li, Wenxuan Deng, Jing Zhao, and Yan Kong review how blending several kinds of spectroscopy sharpens food quality testing, in a 2026 paper. One light-based method alone often lacks the precision for complex foods. Fusion mixes multiple spectra, and sometimes other data, at low, middle, or high levels of processing. The combined view catches more of a sample's chemistry than any single scan.

The review reports strong results in spotting adulteration, tracing where foods come from, and grading quality. It also lists the obstacles: data from different machines must agree, equipment is hard to unite, and models trained in one lab may fail in another. The authors look ahead to lighter devices, deep learning woven into the pipeline, shared standards, and cleaner data workflows. They frame fusion as the path to smart, reliable food analysis.

Catalog record

Source: https://europepmc.org/articles/PMC13147839