ReviewFoods (Basel, Switzerland)2025
Deep Learning-Driven Intelligent Fluorescent Probes: Advancements in Molecular Design for Accurate Food Safety Detection.
Review in Foods (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- More Than a Glow: How Cyanine Dyes Shape Tracer Pharmacokinetics.Angewandte Chemie (International ed. in English) · 2026Review
- Carbon Quantum Dots-Enhanced Polyvinyl Alcohol: A Comprehensive Approach to Food Preservation.Comprehensive reviews in food science and food safety · 2026Review
- A Dicyanoisophorone-Naphthalimide-Based Ratiometric Fluorescent Probe for the Detection of Cysteine and Its Applications.Luminescence : the journal of biological and chemical luminescence · 2026Article
- Construction of a Near-Infrared Viscosity Fluorescent Probe Targeting Mitochondria and Study on Its pH-Coordinated Effect.Molecules (Basel, Switzerland) · 2026Article
- Bio-Based Smart Packaging Materials for Next-Generation Food Systems.Materials (Basel, Switzerland) · 2026Review
- A General Analytic Approach for Rapid Diagnostics by a Simple Algorithm for Fluorescence Single Molecule Counting.Biosensors · 2026Article
- A Contrastive-Learning-Based Pre-Training Framework for Optical Property Prediction of Low-Data Rhodamines with Interpretable Multitask Graph Neural Networks.Molecules (Basel, Switzerland) · 2026Article
- Progress in Electrochemical and Fluorescence Sensors for Propyl Gallate Monitoring in Food Samples.Biosensors · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The complexity of global food supply chains challenges public health, requiring advanced detection technologies beyond traditional lab methods. Fluorescent sensing, known for its sensitivity and quick response, is promising for food safety but hindered by inefficient probe design and difficulties in analyzing complex signals in food. Deep Learning (DL) offers solutions with its nonlinear modeling and pattern recognition capabilities. This review explores recent advancements in DL applications for fluorescent sensing. We explore deep learning methods for predicting fluorescent probe properties and generating fluorescent molecule structures, highlighting their role in accelerating high-performance probe development. We then offer a detailed discussion on the pivotal technologies of deep learning in the intelligent analysis of complex fluorescent signals. On this basis, we engage in a thorough reflection on the core challenges presently confronting the field and propose a forward-looking perspective on the future developmental trajectories of fluorescent sensing technology, offering a comprehensive and insightful roadmap for future research in this interdisciplinary domain.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.