FT-IR and untargeted chemometric analysis for adulterant detection in chia and sesame oils
Chia (Salvia hispanica L.) and sesame (Sesamum indicum L.) oils are valorized for their health benefits and both are extensively used as ingredients in different food formulations and/or processes. Their retail prices are higher than those of other edible oils and might promote fraudulent adulterati...
| Main Authors: | , , , , |
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| Format: | info:ar-repo/semantics/artículo |
| Language: | Inglés |
| Published: |
Elsevier
2019
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| Subjects: | |
| Online Access: | https://www.sciencedirect.com/science/article/pii/S0956713519302336 http://hdl.handle.net/20.500.12123/5224 https://doi.org/10.1016/j.foodcont.2019.05.025 |
| Summary: | Chia (Salvia hispanica L.) and sesame (Sesamum indicum L.) oils are valorized for their health benefits and both are extensively used as ingredients in different food formulations and/or processes. Their retail prices are higher than those of other edible oils and might promote fraudulent adulterations. Spectroscopic methods associated to untargeted analysis are appropriate and faster than traditional techniques, requiring less time to prepare and run the samples. In the present study Fourier transform infrared spectroscopy was used in combination with one class partial least squares and soft independent modelling by class analogy to detect the presence of four possible adulterants: corn, peanut, soybean and sunflower oils, in four different proportions (pure + adulterant: 90 + 10, 95 + 5, 98 + 2 and 99 + 1, in volume). Untargeted approaches were successful in the detection of adulterated chia and sesame oils with acceptable prediction errors ranging between 1% and 5%. |
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