Rice origin traceability using mid-infrared and fluorescence spectral data fusion

作者：Chang Ming Li, Yong Tan, Chunyu Liu, Xun Gao, Zhong Lv, Hongchen Zhang, Yong Zhang · 发表于：Frontiers in Plant Science · 年份：2025 · DOI：10.3389/fpls.2025.1679754 · 被引用次数：2 · 研究领域：Spectroscopy and Chemometric Analyses、Remote Sensing in Agriculture、Smart Agriculture and AI

This study overcomes the limitations of traditional single-spectroscopy techniques by constructing an intelligent discrimination system for rice geographic origin that integrates mid-infrared (MIR) and fluorescence (FLU) spectral feature fusion with machine learning. Using the “Zhongke Fa 5” rice variety from eight major production regions in Jilin Province, China, as the research object, spectral data were acquired using Fourier transform infrared (FTIR) and fluorescence spectrometers. A “Normalization-Smoothing-Multiplicative Scatter Correction” preprocessing framework was proposed, significantly enhancing the signal-to-noise ratio and separability of the spectral features. The complementary characteristics of the multispectral data were elucidated: MIR spectra (500–3750 cm -1 ) accurately represented molecular vibration features of key components such as starch, protein, and lipids, while FLU spectra (450–850 nm) effectively captured the fluorescence characteristics of phenolic compounds and protein-pigment complexes. The successive projections algorithm (SPA) was employed to extract 286–310 highly discriminative features from the original 7625-dimensional data, effectively mitigating the overfitting problem associated with high-dimensional data. The performance differences between data-level and feature-level fusion strategies were compared. The feature-level fusion model optimized by SPA demonstrated significant advantages, achieving a test set accuracy of 95.55%. Regard...