ArticleSensors (Basel, Switzerland)2025
Pressure-Guided LSTM Modeling for Fermentation Quantification Prediction.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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
2 citing papers in PubMed.
- From observable fermentation data to hidden cell states: A modeling study of a mixotrophic Clostridium coculture under perfusion mode.PLoS computational biology · 2026Article
- Artificial intelligence in bread making: Applications in quality control, formulation and sensory prediction.Food chemistry: X · 2026Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite significant advancements in sensor technologies, real-time monitoring and prediction of fermentation dynamics remain challenging due to the complexity and nonlinearity of environmental variables. This study presents an integrated framework that combines deep learning techniques with blockchain-enabled data logging to enhance the reliability and transparency of fermentation monitoring. A Long Short-Term Memory (LSTM)-based Fermentation Process Prediction Model (FPPM) was developed to predict Fermentation Percent (
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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.