Colloidal Fingerprints of Food and Beverages with multidetector AF4-MALS

Food analysis is crucial for consumer safety, authenticity, and pollutant detection. Benchmark analytical methods use complex protocols and organic solvents, and more sustainable alternatives are to be sought out. A recent trend in analytical chemistry leverages raw data from experiments (e.g., chromatographic profiles, spectra) as sample fingerprints, analyzed with multivariate tools to predict characteristics (e.g., origin, composition) and identify adulterations. These approaches reduce analyses, lab work, and resource use, offering greener, more cost-effective alternatives.

The webinar will show how to combine the two aims, sustainability and data maximization, using an aqueous colloidal system, developing AF4-DAD-MALS-dRI-FLD platforms to separate and characterize components from food matrices:

• Red wine profiling and in-depth analysis of output data can highlight particulate complexity, explain sensory feedback, and lead to cultivar grouping.

• Milk analysis and multivariate elaboration of fractograms enabled single-run discrimination of factors like thermal treatment, fat content, and even the manufacturing plant’s impact on colloidal content.

These AF4 method are fast, uses minimal sample preparation, avoid chemical treatments, offering a greener, faster and possibly cheaper alternative. The FFF approach can enhance food quality control and fraud detection processes and is adaptable to other colloidal food samples. Additionally, it underscores the need to increase multivariate analysis in data from FFF platforms.