Work

Rapid identification of the geographical origins of lily bulbs using solid-surface three-dimensional fluorescence spectroscopy coupled with multi-way pattern recognition

Year: 2026

Type: article

Abstract: In this work, solid-surface three-dimensional fluorescence (SSTF) spectroscopy coupled with multi-way pattern recognition was proposed to identify the origins of lily bulbs. Firstly, the spectral data of lily bulbs from 8 famous regions were acquired by SSTF within the excitation wavelength of 200–700 nm and emission wavelength of 250–700 nm in solid power state without any extraction processes. Then, a parallel factor analysis (PARAFAC) model was built to extract chemically relevant components information, and the resulting scores were classified using linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), and k-nearest neighbor ( k -NN); while N-way partial least squares-discriminant analysis (N-PLS-DA), unfolded partial least squares-discriminant analysis (U-PLS-DA), and two-directional two-dimensional linear discriminant analysis ((2D) 2 LDA) were directly used for classification based on the original SSTF spectral data. All models showed satisfactory performance, with the latter three models achieving 100% accuracy for both training and prediction sets. Therefore, the proposed method can achieve direct and rapid origin identification of lily bulbs without solvent-extraction and provide a new perspective for the origin identification of other food and medicinal materials. • Solid-surface three-dimensional fluorescence was first used for origin identification of lily bulbs. • Multi-way pattern recognition algorithms were employed to model the spectral data of lily bulbs. • Direct spectral pattern recognition based on raw fluorescence data showed the best performances. • The proposed origin identification method was rapid and simple without solvent-extraction steps. • It provided a new perspective for the origin identification of food and medicinal materials.

Source: Journal of Food Composition and Analysis

DOI: 10.1016/j.jfca.2026.108984