ReviewFood chemistry: X2026
Spectroscopy-multimodal data fusion empowers smart food quality analysis: Challenges and prospects.
Review in Food chemistry: X, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Food Intelligent Quality and Safety Analysis: From Data-Driven to Data-Mechanism Hybrid-Driven Paradigm.Foods (Basel, Switzerland) · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Food quality assessment faces challenges in detecting complex samples, with single spectroscopic techniques limited in precision and applicability. Multimodal data fusion, by combining different spectroscopic techniques with non-spectroscopic data, significantly enhances the accuracy and comprehensiveness of detection. Low-, mid-, and high-level data fusion strategies offer substantial advantages in information integration and model optimization, addressing the shortcomings of single spectroscopic methods in complex food sample analysis. Multimodal data fusion has shown great potential in food adulteration detection, geographical traceability, and quality assessment. However, current technologies still face challenges such as data consistency, equipment integration, and model generalization. Future research will focus on lightweight equipment development, deep learning integration, standardization, and optimized data processing workflows to advance the development and application of food quality detection technologies.
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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.