• Medientyp: E-Artikel
  • Titel: Spectroscopic-Based Prediction of Milk Foam Properties for Barista Applications
  • Beteiligte: Christin Brettschneider, Kim; Zettel, Viktoria; Sadeghi Vasafi, Pegah; Hummel, Darius; Hinrichs, Jörg; Hitzmann, Bernd
  • Erschienen: Springer Science and Business Media LLC, 2022
  • Erschienen in: Food and Bioprocess Technology
  • Sprache: Englisch
  • DOI: 10.1007/s11947-022-02822-3
  • ISSN: 1935-5130; 1935-5149
  • Schlagwörter: Industrial and Manufacturing Engineering ; Process Chemistry and Technology ; Safety, Risk, Reliability and Quality ; Food Science
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  • Beschreibung: <jats:title>Abstract</jats:title><jats:p>The important quality parameters of cow’s milk for barista applications are frothability and foam stability. In the past, quality assessment was very time-consuming and could only be carried out after milk treatment had been completed. Since spectroscopy is already established in dairies, it could be advantageous to develop a spectrometer-based measurement method for quality control for barista applications. By integrating online spectroscopy to the processing of UHT (ultra-high temperature processing) milk before filling, it can be checked whether the currently processed product is suitable for barista applications. To test this hypothesis, a feasibility study was conducted. For this purpose, seasonal UHT whole milk samples were measured every 2 months over a period of more than 1 year, resulting in a total of 269 milk samples that were foamed. Samples were frothed using a self-designed laboratory frother. Frothability at the beginning and foam loss after 15 min describe the frothing characteristics of the milk and are predicted from the spectra. Near-infrared, Raman, and fluorescence spectra were recorded from each milk sample. These spectra were preprocessed using 15 different mathematical methods. For each spectrometer, 85% of the resulting spectral dataset was analyzed using partial least squares (PLS) regression and nine different variable selection (VS) algorithms. Using the remaining 15% of the spectral dataset, a prediction error was determined for each model and used to compare the models. Using spectroscopy and PLS modeling, the best results show a prediction error for milk frothability of 3% and foam stability of 2%.</jats:p>