Explainable artificial intelligence (XAI) applied to deep computer vision for the assessment and classification of oleogels with varying oleogelator types and concentrations

Oleogels represent a promising alternative to trans and saturated fats, as they enable the structuring of liquid oils into semi-solid systems while improving nutritional quality, texture, and stability. Their development relies on oleogelators, which are considered food additives and must be carefully selected and regulated due to differences in functionality, cost, and acceptance.We investigated structural variations in oleogels produced with different oleogelator types and concentrations, using polarized light microscopy images coupled with deep learning and explainable artificial intelligence (XAI). A convolutional neural network (CNN) was employed to classify the samples and explore the relationship between crystalline organization and oil-holding capacity. CNN achieved 95 % accuracy in distinguishing between oleogels formulated with beeswax (BW) and glycerol monostearate (GM), and 77 % accuracy in classifying their concentrations. Morphological analyses revealed that BW-based oleogels form platelet-shaped crystals, while GM-based oleogels exhibit irregular clusters of needle-shaped crystals. XAI, using Grad-CAM++, highlighted that CNN predictions were primarily based on these structural features, particularly at lower concentrations. At higher concentrations (≥6 % w /w), similarities between BW and GM reduced classification accuracy. Overall, integrating microscopy with deep learning and XAI proved to be a robust and innovative methodology for characterizing oleogel structures, offering valuable insights for formulation and development. • -Oleogels were discriminated between oleogels produced with BW or GM with 95 % accuracy. • XAI applied to images highlighted the crystalline conformation as regions of interest. • Crystalline conformation changes depending on the oleogelator and its concentration. • Oil holding capacity varies according to crystalline conformation.

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Publication Details

Journal
Microchemical Journal
Published
2026-01-04
DOI
https://doi.org/10.1016/j.microc.2026.116821
Primary Topic
Food Chemistry and Fat Analysis
Type
article
Field-Weighted Citation Impact
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article

Explainable artificial intelligence (XAI) applied to deep computer vision for the assessment and classification of oleogels with varying oleogelator types and concentrations

Ingrid A. Moraes, Douglas Fernandes Barbin, Leonardo Arrighi, Rosiane Lopes Cunha
Microchemical Journal
Food Chemistry and Fat Analysis
article

Explainable artificial intelligence (XAI) applied to deep computer vision for the assessment and classification of oleogels with varying oleogelator types and concentrations

Ingrid A. Moraes, Douglas Fernandes Barbin, Leonardo Arrighi, Rosiane Lopes Cunha
article en

Abstract

Oleogels represent a promising alternative to trans and saturated fats, as they enable the structuring of liquid oils into semi-solid systems while improving nutritional quality, texture, and stability. Their development relies on oleogelators, which are considered food additives and must be carefully selected and regulated due to differences in functionality, cost, and acceptance.We investigated structural variations in oleogels produced with different oleogelator types and concentrations, using polarized light microscopy images coupled with deep learning and explainable artificial intelligence (XAI). A convolutional neural network (CNN) was employed to classify the samples and explore the relationship between crystalline organization and oil-holding capacity. CNN achieved 95 % accuracy in distinguishing between oleogels formulated with beeswax (BW) and glycerol monostearate (GM), and 77 % accuracy in classifying their concentrations. Morphological analyses revealed that BW-based oleogels form platelet-shaped crystals, while GM-based oleogels exhibit irregular clusters of needle-shaped crystals. XAI, using Grad-CAM++, highlighted that CNN predictions were primarily based on these structural features, particularly at lower concentrations. At higher concentrations (≥6 % w /w), similarities between BW and GM reduced classification accuracy. Overall, integrating microscopy with deep learning and XAI proved to be a robust and innovative methodology for characterizing oleogel structures, offering valuable insights for formulation and development. • -Oleogels were discriminated between oleogels produced with BW or GM with 95 % accuracy. • XAI applied to images highlighted the crystalline conformation as regions of interest. • Crystalline conformation changes depending on the oleogelator and its concentration. • Oil holding capacity varies according to crystalline conformation.

Microchemical JournalVol. 221
University of Trieste (IT), Universidade Estadual de Campinas (UNICAMP) (BR)
Openalex Percentile: Top 97%
Food Chemistry and Fat Analysis
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