ArticleNPJ science of food2026
Unveiling key peak features for olive oil authentication utilizing Raman spectroscopy and chemometrics.
Article in NPJ science of food, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- A Multi-Task Learning Model Based on DTP-MMoE for Identification of Olive Oil Multi-Adulteration Using Raman Spectroscopy.Foods (Basel, Switzerland) · 2026Article
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Authors and funding
5 authors.
Funding
Abstract
Adulteration of olive oil significantly compromises the interests of both producers and consumers, making its authentication a crucial challenge in the food industry. This study explored the potential of combining Raman spectroscopy with machine learning for discriminating various blended samples and quantifying olive oil content in mixtures. Raman features, such as peak intensities at specific shifts, were extracted from the spectra and analyzed using hierarchical cluster analysis (HCA) and correlation analysis (CA) to identify significant variations corresponding to altered proportions of olive oil. Qualitative and quantitative analyses were performed to classify 10 oil types and predict compositional ratios in binary and ternary blends, comparing different chemometric techniques and input features. Among these, the random forest (RF) model yielded a high classification accuracy (98.9%) and strong predictive performance, with coefficients of determination (R
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