ArticleJournal of the science of food and agriculture2025
Non-destructive quantification of egg yolk ratio using visible-near-infrared hyperspectral imaging, machine learning and explainable AI.
Article in Journal of the science of food and agriculture, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Rapid Determination of Soybean Protein Content by Near-Infrared Spectroscopy Coupled with Multi-Learner Ensemble Wavelength Selection.Foods (Basel, Switzerland) · 2026Article
- Cross-Temporal Egg Variety and Storage Period Classifications via Multi-Task Deep Learning with Near-Infrared Hyperspectral Imaging.Foods (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundAccurate, non-destructive quantification of egg yolk ratio holds considerable significance for the food industry, nutritional assessment and egg grading. Conventional approaches are limited by destructive testing and insufficient throughput for commercial applications. This study investigates the potential of visible-near-infrared (Vis-NIR; 374-1015 nm) hyperspectral imaging (HSI) combined with machine learning (ML) and explainable artificial intelligence (AI) techniques for rapid and non-destructive yolk ratio prediction. Multiple regression models, spectral preprocessing and feature selection techniques were comprehensively evaluated to develop robust and interpretable predictive solutions.
resultsRegression models including partial least squares regression (PLSR), random forest, extreme gradient boosting and support vector regression were assessed for yolk ratio prediction. The PLSR model combined with Savitzky-Golay first-derivative spectral preprocessing demonstrated superior and stable predictive performance, achieving coefficients of determination (R
conclusionThis research highlights the effectiveness of Vis-NIR HSI integrated with ML and explainable AI as a rapid, reliable and non-destructive approach for egg yolk ratio assessment. The developed method offers significant advantages for egg quality monitoring, providing practical, interpretable and scalable solutions beneficial for food industry applications. © 2025 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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Registered trials
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