Evidence map›Paper›PMID 41939140›Full record

ArticleCurrent research in food science2026

Rapid single-cell identification of foodborne pathogens with limited data: A peak-aware Raman attention deep learning model.

Jiazheng Sun, Sen Wang, Hua Xia, Yingxiao Peng, Feng Gao, Yi Sun, Jiayi Wang, Xiangyun Ma, Qifeng Li

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Article in Current research in food science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Jiazheng SunState Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin, 300072, China.
Sen WangState Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin, 300072, China.
Hua XiaState Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin, 300072, China.
Yingxiao PengState Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin, 300072, China.
Feng GaoState Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin, 300072, China.
Yi SunState Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin, 300072, China.
Jiayi WangState Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin, 300072, China.
Xiangyun MaState Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin, 300072, China.
Qifeng LiState Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin, 300072, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Foodborne pathogens represent a major global threat to food safety, necessitating rapid and reliable identification technologies. While Raman spectroscopy provides a powerful label free tool for molecular fingerprinting, its performance is often compromised by high biochemical similarity between strains and the bottlenecks of data scarcity in practical scenarios. To address these challenges, we propose the Peak Aware Raman Attention Model (PARAM), a generative deep learning framework designed for high fidelity spectral augmentation. Unlike traditional generative models that often suffer from peak shifts or intensity distortions, PARAM integrates a peak aware attention mechanism. This mechanism explicitly preserves the structural integrity of biochemically significant spectral regions, such as the 1003 cm

Indexed as

Data augmentationDeep learningFoodborne pathogensFood safetyRaman spectroscopy

Identifiers

PMID41939140
PMCPMC13049411

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.