AI-driven optimization of bioactive compounds extraction from food byproducts: research progress and application prospect
Bibliographic record
Abstract
Food byproducts are rich in bioactive compounds with nutritional and preservation benefits, yet conventional extraction methods are limited by high solvent use, time-consuming, and poor scalability. Green technologies such as ultrasound-assisted extraction (UAE), supercritical fluid extraction (SFE), and microwave-assisted extraction (MAE) offer sustainable alternatives but require precise optimization of complex, non-linear parameters. This review highlights artificial intelligence (AI) as a transformative tool to overcome these challenges by enabling predictive modeling, real-time optimization, and intelligent process control of extraction processes. Unlike previous reviews focusing mainly on green extraction techniques, this work uniquely synthesizes recent progress on AI-driven approaches. It critically compares their performance against traditional methods such as response Surface methodology. Case studies include how AI models, including artificial neural networks, support vector regression, and hybrid algorithms, deliver higher yields, lower energy use, and improved reproducibility. The review further addresses industrial applications, regulatory gaps, and commercialization challenges, offering future research directions for scalable and interpretable AI frameworks. Combining sustainability, efficiency, and innovation, this review positions AI-driven extraction as a frontier for advancing functional food development and circular bioeconomy strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".