ArticleCurrent research in food science2022
Prediction of banana maturity based on the sweetness and color values of different segments during ripening.
Article in Current research in food science, 2022. 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
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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.
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Who cites it
3 citing papers in PubMed, 31 citations in OpenAlex.
- Designing of guava quality classification model based on ANOVA and machine learning.Scientific reports · 2025Article
- Explainable AI and mobile imaging for non-destructive avocado ripeness and internal quality assessment to reduce food waste.Current research in food science · 2025Article
- Banana and Guava dataset for machine learning and deep learning-based quality classification.Data in brief · 2024Article
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To predict the maturity of bananas, the present study used non-destructive methods to analyze changes in the sweetness and color of the stalks, middles, and tips of bananas during ripening. The results indicated that the respective maturation of these three segments did not occur simultaneously, as indicated by the differential enzyme activity and gene expression levels recorded in these segments. A principal component analysis and cluster plots were used to review the classification of banana maturity, highlighting that banana maturation can be divided into six stages. Two distinct maturity prediction algorithms were established using random forest, artificial neural network, and support vector machines, and they also indicated that dividing the maturity of bananas into six stages was adequate. These findings contribute to the development of quality evaluation and of a rapid grading system for processing, which improves the quality and sale of banana fruits and the related processed products.
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Registered trials
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