Enhancing Fritillariae Cirrhosae Bulbus authentication via Mask-AIR: a masked autoencoder driven information reconstruction and iterative attention framework for multi-view fusion.
6 affiliations
- 1Department of Pharmacy, Affiliated Hospital of Southwest Jiaotong University, The Third People's Hospital of Chengdu, Chengdu, Sichuan, China.
- 2Department of Pharmacy, Sichuan Provincial People's Hospital East Sichuan Hospital & Dazhou First People's Hospital, Dazhou, China.
- 3Department of Otolaryngology Head and Neck Surgery, West China Hospital, Sichuan University, Chengdu, China.
- 4School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, China.
- 5School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
- 6Center for Digital Intelligence and Innovation (CDII), Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Abstract
Fritillariae Cirrhosae Bulbus (FCB), a valuable medicinal and edible plant with antitussive properties, is frequently adulterated due to resource scarcity and high market price, necessitating rapid and accurate authentication. However, existing methods (morphological, molecular, chromatographic, etc.) are subjective, destructive, or suffer from DNA degradation. To address these limitations, this paper proposes a multi-modal information reconstruction framework based on a masked autoencoder (MAE) and an iterative attention mechanism that integrates a multi-view fusion strategy using three views (hyperspectral imaging, grayscale images, and pseudo-colour images) to enhance the authenticity identification of FCB. Specifically, multi-view data are fused to enrich information dimensions and thereby improve species recognition accuracy. An MAE operating in the latent feature space reconstructs randomly masked tokens from each modality, which enhances feature representation and mitigates cross-modal interference. Subsequently, hierarchical iterative attention module alternates query roles among modalities to balance fusion. Feature extraction, reconstruction, and classification are trained end-to-end, while inference retains only the encoder and classifier, incurring no extra cost. The experiments on a self-built FCB multi-view dataset show that the proposed method significantly improves classification accuracy across all categories and outperforms both conventional and deep learning baselines. Therefore, this work provides an effective non-destructive tool for FCB authenticity identification and can be extended to the authentication of other valuable functional foods.
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BibTeX
@article{du2026enhancing,
title = {{Enhancing Fritillariae Cirrhosae Bulbus authentication via Mask-AIR: a masked autoencoder driven information reconstruction and iterative attention framework for multi-view fusion.}},
author = {Huan Du and Xiu Gu and Hai Zhu and Yu Chen and Xin Wang and Xiaoqin Zhi and Ziqin Lei and Xiaohui Qu and Gang Fan},
journal = {Frontiers in nutrition},
year = {2026},
month = oct,
doi = {10.3389/fnut.2026.1928119},
url = {https://europepmc.org/article/MED/42898371},
note = {CC BY},
}
RIS
TY - JOUR
TI - Enhancing Fritillariae Cirrhosae Bulbus authentication via Mask-AIR: a masked autoencoder driven information reconstruction and iterative attention framework for multi-view fusion.
AU - Huan Du
AU - Xiu Gu
AU - Hai Zhu
AU - Yu Chen
AU - Xin Wang
AU - Xiaoqin Zhi
AU - Ziqin Lei
AU - Xiaohui Qu
AU - Gang Fan
PY - 2026
DA - 2026/10/05
JO - Frontiers in nutrition
DO - 10.3389/fnut.2026.1928119
UR - https://europepmc.org/article/MED/42898371
AB - Fritillariae Cirrhosae Bulbus (FCB), a valuable medicinal and edible plant with antitussive properties, is frequently adulterated due to resource scarcity and high market price, necessitating rapid and accurate authentication. However, existing methods (morphological, molecular, chromatographic, etc.) are subjective, destructive, or suffer from DNA degradation. To address these limitations, this paper proposes a multi-modal information reconstruction framework based on a masked autoencoder (MAE) and an iterative attention mechanism that integrates a multi-view fusion strategy using three views (hyperspectral imaging, grayscale images, and pseudo-colour images) to enhance the authenticity identification of FCB. Specifically, multi-view data are fused to enrich information dimensions and thereby improve species recognition accuracy. An MAE operating in the latent feature space reconstructs randomly masked tokens from each modality, which enhances feature representation and mitigates cross-modal interference. Subsequently, hierarchical iterative attention module alternates query roles among modalities to balance fusion. Feature extraction, reconstruction, and classification are trained end-to-end, while inference retains only the encoder and classifier, incurring no extra cost. The experiments on a self-built FCB multi-view dataset show that the proposed method significantly improves classification accuracy across all categories and outperforms both conventional and deep learning baselines. Therefore, this work provides an effective non-destructive tool for FCB authenticity identification and can be extended to the authentication of other valuable functional foods.
SN - 2296-861X
ER -