High-throughput silica nanoparticle detection for quality control of complex early life nutrition food matrices
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2024
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01A - Journal article
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ACS Omega
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American Chemical Society
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Abstract
The addition of nanomaterials to improve product properties has become a matter of course for many commodities: e.g., detergents, cosmetics, and food products. While this practice improves product characteristics, the increasing exposure and potential impact of nanomaterials (<100 nm) raise concerns regarding both the human body and the environment. Special attention should be taken for vulnerable individuals such as those who are ill, elder, or newborns. But detecting and quantifying nanoparticles in complex food matrices like early life nutrition (ELN) poses a significant challenge due to the presence of additional particles, emulsion-droplets, or micelles. There is a pressing demand for standardized protocols for nanoparticle quantification and the specification of “nanoparticle-free” formulations. To address this, silica nanoparticles (SiNPs), commonly used as anticaking agents (AA) in processed food, were employed as a model system to establish characterization methods with different levels of accuracy and sensitivity versus speed, sample handling, and automatization. Different acid treatments were applied for sample digestion, followed by size exclusion chromatography. Morphology, size, and number of NPs were measured by transmission electron microscopy, and the amount of Si was determined by microwave plasma atomic emission spectrometry. This successfully enabled distinguishing SiNP content in ELN food formulations with 2–4% AA from AA-free formulations and sorting SiNPs with diameters of 20, 50, and 80 nm. Moreover, the study revealed the significant influence of the ELN matrix on sample preparation, separation, and characterization steps, necessitating method adaptations compared to the reference (SiNP in water). In the future, we expect these methods to be implemented in standard quality control of formulation processes, which demand high-throughput analysis and automated evaluation.
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2470-1343
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English
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Yes
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Published
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Gold
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Maffeis, V., Otter, A., Düsterloh, A., Kind, L., Palivan, C., & Saxer, S. (2024). High-throughput silica nanoparticle detection for quality control of complex early life nutrition food matrices. ACS Omega. https://doi.org/10.1021/acsomega.3c09459