ArticlePeerJ2025
Machine learning classification of mango maturity based on carotene content from Raman spectra.
Article in PeerJ, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Determining mango ripeness is essential for ensuring its delicious taste, enticing aroma, and rich nutritional value. For farmers, harvesting mangoes too early can result in stunted fruit and lower yields compared to those harvested at a ripe stage. This study aims to develop a potentially non-invasive and efficient method for detecting mango ripeness using Raman spectroscopy. Traditional methods, which rely on human assessment and color evaluation with image processing, are inconsistent, inaccurate, and time-consuming due to variations in mango color and individual differences in vision and perception. To address these limitations, this study pursued three main objectives: extracting data characteristics of organic compounds in mangoes based on raw Raman spectrum data, identifying the correlation between carotene characteristics and mango ripeness levels, and evaluating the performance of machine learning models in classifying mango ripeness levels. A total of 29 mango fruit spectra were analyzed, with 13 samples selected to represent three ripeness categories: underripe, ripe, and overripe. Raman spectra peak signal analysis revealed that mango peel contains lycopene, β-carotene, lutein, and neoxanthin, all of which are derived from carotenoid molecules in the range of 1,480 cm
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