ArticleACS omega2025
New Analytical Strategies for Quality Control and Classification of Apple Juices Using Digital Image Processing (DIP) Combined with Machine Learning (ML).
Article in ACS omega, 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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Abstract
Apple juice is widely consumed for its pleasant sensory attributes and nutritional value. However, due to its high commercial value, this beverage, particularly whole juice, is susceptible to fraud and adulteration. This underscores the need for rapid, noninvasive analytical strategies to ensure product authenticity and quality. This study reports, for the first time, the application of smartphone-based image analysis combined with machine learning as a low-cost and nondestructive approach for both classifying apple juice types and predicting the actual juice content in apple-based beverages. Images of nine whole juice (WJ), four reconstituted juice (RE), and five nectar (NE) samples were analyzed to develop models capable of discriminating beverage categories and estimating apple juice concentration. Classification models generated using
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Registered trials
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