3rd International Conference on Chemo and BioInformatics, Kragujevac, September 25-26. 2025. (pp. 632-635)
АУТОР(И) / AUTHOR(S): Marijana Ačanski, Marko Ilić, Djura Vujić, Kristian Pastor
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DOI: 10.46793/ICCBIKG25.632A
САЖЕТАК / ABSTRACT:
Authentication of cereal-based food products has become increasingly important due to the prevalence of food fraud, mislabeling, and the demand for products labeled as gluten-free, wholegrain, or originating from specific regions. These issues not only affect consumer trust but also have significant implications for health, trade, and regulatory compliance. Chemoinformatics, which integrates chemical profiling with multivariate statistical and machine learning techniques, offers a powerful solution for verifying the authenticity and integrity of cereals, pseudocereals, flours, breads, and other bakery products.
This mini-review presents an overview of recent advances in chemoinformatics methodologies used for food authentication. It highlights key analytical platforms, such as gas chromatography–mass spectrometry (GC-MS), near-infrared (NIR) spectroscopy, Fourier-transform infrared (FTIR) spectroscopy, and inductively coupled plasma–mass spectrometry (ICP-MS). Data derived from these techniques are often processed using principal component analysis (PCA), partial least squares discriminant analysis (PLS- DA), support vector machines (SVM), clustering, and other classification algorithms.
We further summarize representative case studies from Europe, Asia, South America, and North Africa that demonstrate the ability of chemoinformatics to differentiate botanical species, trace geographical origins, and detect adulteration in cereal-based matrices. The review outlines current challenges in method standardization and model validation, while emphasizing the future potential of portable devices and artificial intelligence in scalable food authentication strategies.
КЉУЧНЕ РЕЧИ / KEYWORDS:
Chemoinformatics, Authentication, Cereals and Pseudocereals, Flour and Bakery
ПРОЈЕКАТ / ACKNOWLEDGEMENT:
This research is funded by the Ministry of Education and Ministry of Science, Technological Development and Innovation, Republic of Serbia, Grants: No. 451-03- 137/2025-03/200134 and 451-03-136/2025-03/200134.
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