@misc{10481/104266,
year = {2025},
url = {https://hdl.handle.net/10481/104266},
abstract = {Hyperspectral imaging is now establishing itself as a transformative analytical technique in the food safety and quality domains, offering unique capabilities for non-destructive, real-time, and high-resolution analysis of food at different levels of its production. Hyperspectral imaging combines the strengths of computer vision and classical spectroscopy. It provides both spatial and spectral information, making it a powerful and green alternative to the conventional techniques employed in this field.
This critical review explores the advances in hyperspectral imaging applications, highlighting its potential to revolutionize food quality and safety assessment, including adulteration, contamination and non-conformity detection. Recent breakthroughs in sensor technology, data processing algorithms, and machine learning integration are discussed, emphasizing the most popular data analysis strategies and their role in addressing the challenges of complex food matrices and dynamic production environments. This review underlines the data analysis approaches applied in each of the collected works, highlighting two trends: studying food samples as a whole or analyzing them as a set of pixel-spectra. Machine learning methods such as principal component analysis, partial least squares regression, partial least squares discriminant analysis, soft independent modelling of class analogy, and support vector machines have been widely applied for the analysis of food samples. These techniques are used for both qualitative and quantitative purposes, regardless of the sample’s origin (plant- or animal-based) or its complexity. Additionally, this review outlines the limitations of hyperspectral imaging, such as high costs, computational demands, and the need for standardized protocols, while identifying opportunities for future research and industrial implementation.},
publisher = {Elsevier},
title = {Strategies for analysing hyperspectral imaging data for food quality and safety issues – A critical review of the last 5 years},
doi = {https://doi.org/10.1016/j.microc.2025.113994},
author = {Medina García, Miriam and Amigo, Jose M. and Martínez Domingo, Miguel Ángel and Valero Benito, Eva María and Jiménez Carvelo, Ana María},
}