Advances in reactive oxygen species detection across biological systems with relevance to postharvest research
Bibliographic record
Abstract
• Overview of ROS detection tools across biological systems and contexts. • Limited imaging use identified in postharvest ROS detection workflows. • IVIS shows promise for studying oxidative stress in fresh produce. • Hybrid techniques offer improved accuracy in oxidative stress detection. • AI integration enhances monitoring and prediction of oxidative stress. Reactive oxygen species (ROS) are molecules that have gained considerable interest in many fields, including biomedical research, plant biology, and postharvest science. Accurate detection and a thorough understanding of their dynamics are essential to unravel the oxidative stress and redox signaling pathways in which they are involved. This review provides a detailed overview of conventional and emerging approaches for ROS detection, comprising spectrophotometry, chromatography, electrochemical detection, fluorescence and luminescence assays, as well as advanced imaging platforms such as confocal laser scanning microscopy (CLSM), fluorescence lifetime imaging microscopy (FLIM), and in vivo imaging system (IVIS). The manuscript also highlights the fundamental principles, advantages, limitations, and various applications across biological systems, with a specific focus on postharvest plant research. Given the inherent limitations of each method in terms of sensitivity, specificity, spatiotemporal resolution, and in vitro or in vivo applicability, this review pointed out the need for multimodal strategies and more specific and stable probes, as well as the integration of ROS detection with omics technologies and AI-based analysis tools. In addition, we pointed out the underutilized potential of imaging platforms such as IVIS for non-invasive, real-time monitoring of ROS in fruits and vegetables during postharvest storage. By linking technology breakthroughs to plant physiology, this review provides insights that may help in choosing appropriate techniques based on experimental objectives and contribute to redox research in plant systems for better postharvest quality management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".