Abstract

CRUZ-TIRADO, J. P.; LOPES DE FRANCA, Pedro Renann  and  FERNANDES BARBIN, Douglas. Chia (Salvia hispanica) seeds degradation studied by fuzzy-c mean (FCM) and hyperspectral imaging and chemometrics - fatty acids quantification. Scientia Agropecuaria [online]. 2022, vol.13, n.2, pp.167-173.  Epub May 18, 2022. ISSN 2077-9917.  http://dx.doi.org/10.17268/sci.agropecu.2022.015.

Chia seeds are nutritious food because they have a high content of protein, polyunsaturated fatty acids (omega-3 and omega-6) and phenolic compounds. During storage, fatty acids are degraded, by oxidative and hydrolytic reactions, forming free fatty acids (FFA). In this work, we used Near Infrared Hyperspectral Imaging (NIR- HSI) and chemometrics to predict FFA acid value and fatty acids concentrations in chia seeds during storage. First, we explore the hyperspectral images by Fuzzy c-means (FCM), where it is possible to observe as chemical compounds are formed or degraded during storage. Second, PLSR models were developed to predict FFA value and fatty acids concentration. RPD values reached values higher then 2.0, indicating a good ability to estimate these chemical compounds, especially polyunsaturated fatty acids omega-3 and omega-6. Finally, NIR-hyperspectral imaging coupled with chemometrics allowed us to show the chemical degradation process of chia seeds during storage, mainly associated with polyunsaturated fatty acids degradation. Besides NIR-HSI showed to be a powerful technique to quantify the main fatty acids with high accuracy.

Keywords : polyunsaturated fatty acids; machine learning; fuzzy c-means; oleaginous seeds.

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