Spectroscopic-Based Prediction of Milk Foam Properties for Barista Applications
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Published:2022-05-11
Issue:8
Volume:15
Page:1748-1757
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ISSN:1935-5130
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Container-title:Food and Bioprocess Technology
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language:en
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Short-container-title:Food Bioprocess Technol
Author:
Christin Brettschneider KimORCID, Zettel Viktoria, Sadeghi Vasafi Pegah, Hummel Darius, Hinrichs Jörg, Hitzmann Bernd
Abstract
AbstractThe 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%.
Funder
Forschungskreis der Ernährungsindustrie Universität Hohenheim
Publisher
Springer Science and Business Media LLC
Subject
Industrial and Manufacturing Engineering,Process Chemistry and Technology,Safety, Risk, Reliability and Quality,Food Science
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1. An Optimized Neural Network Model to Predict Milk Quality;2024 14th International Conference on Cloud Computing, Data Science & Engineering (Confluence);2024-01-18 2. Correction to: Spectroscopic‑Based Prediction of Milk Foam Properties for Barista Applications;Food and Bioprocess Technology;2022-06-18
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