Kunshan Yao

Hyperspectral Imaging Combined With a Hybrid Deep Learning Framework for Simultaneous Monitoring of Multiple Quality Indicators in Frozen-thawed Beef

Advances in Non-Destructive Detection Technologies for Seed Quality: A Review

Seed quality profoundly affects productivity, marketability, and food security, yet conventional evaluation methods are destructive, slow, and unsuited to high-throughput screening. Non-destructive techniques, being rapid, non-invasive, and capable of measuring multiple indicators, have therefore gained substantial mom...

SERS-Based Detection of Food Contaminants: From Laboratory Sensitivity to Practical Implementation—Bottlenecks and Pathways to Standardization

Ensuring food safety requires the detection of trace-level contaminants such as pesticides, mycotoxins, and heavy metals. Analytical approaches for these analytes should feature high sensitivity, good selectivity, and compatibility with aqueous matrices; surface-enhanced Raman spectroscopy (SERS) satisfies these requir...

Non-destructive prediction of lead content in oilseed rape leaves by fluorescence hyperspectral technology based on neural network.

Based on fluorescence hyperspectral imaging (FHSI), this study targeted rapid, non-destructive quantification of lead (Pb) content in oilseed rape leaves treated with varying silicon (Si) concentrations, acquiring fluorescence spectra over the 484.43-1001.61 nm wavelength range. To optimize spectral data quality, prepr...