ReviewJournal of food science and technology2025
An analysis on detection of artificially ripened fruits.
Review in Journal of food science and technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nowadays everyone is prioritising their health and investing in maintaining it. People understand the value of a nutritious diet, with fruits being a key component due to their abundance of essential vitamins and minerals necessary for good health. However, there's growing concern over using artificial ripening agents on fruits, which can cause health hazards. This paper examines various approaches to identify artificially ripened fruits, considering their effectiveness, limitations, and applicability in ensuring food safety and consumer health. The analysis aims to contribute to the enhancement of fruit quality control measures and the prevention of health hazards associated with the consumption of artificially ripened fruits. This paper proposes a novel approach which combines triad spectroscopy, TGS 2600 gas sensor and Raspberry Pi-based image processing to detect artificially ripened fruits, which has not been explored yet. This integration will offer a promising theoretical solution, with the potential for real-time detection of artificially ripened fruits. The proposed approach will improve detection accuracy and provide a cost-effective method.
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Registered trials
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