ArticleFood chemistry: X2025
Artificial intelligence and machine learning models for predicting and evaluating the influence of shelf-life environments and packaging materials on garlic (
Article in Food chemistry: X, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Recent advances in sulfur-containing flavors of garlic (Food chemistry: X · 2026Review
- Computational intelligence applications in predicting energy consumption, greenhouse gas emissions, and drying performance of hybrid infrared dryer.Scientific reports · 2026Article
- A Machine Learning Model to Reframe the Concept of Shelf-Life in Bakery Products: PDO Sourdough as a Technological Preservation Model.Foods (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The nutritional content and quality of garlic, a crop widely consumed, must be preserved after harvesting by overcoming several challenges. The necessity of this study arises from the growing demand for effective postharvest management solutions that can extend shelf life, maintain the nutritional integrity of garlic and enhance consumer satisfaction. This study explores the application of machine learning (ML) and artificial intelligence (AI) to predict the effects of storage environments and packaging materials on garlic's physicochemical properties, enzymatic activities, phytochemical content, and antioxidant characteristics. The results show that temperature significantly influenced most parameters, except for anthocyanin, and that packaging impacted all variables. Storing garlic at 4 °C was found to preserve its quality better than at 25 °C, offering insights into optimizing storage conditions and packaging for superior product quality. This research provides valuable guidance on controlling factors that affect garlic's postharvest performance, aiming to improve preservation and reduce food waste.
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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.