Institut für Lebensmittelwissenschaft und Biotechnologie

Permanent URI for this collectionhttps://hohpublica.uni-hohenheim.de/handle/123456789/6

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Browsing Institut für Lebensmittelwissenschaft und Biotechnologie by Journal "Cereal chemistry"

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    Fluorescence spectroscopy of flour fractions and dough: analysis of spectral differences and potential to improve wheat quality prediction
    (2025) Ziegler, Denise; Buck, Lukas; Scherf, Katharina Anne; Popper, Lutz; Hitzmann, Bernd; Ziegler, Denise; Department of Process Analytics, Institute of Food Science and Biotechnology, University of Hohenheim, Stuttgart, Germany; Buck, Lukas; Department of Bioactive and Functional Food Chemistry, Institute of Applied Biosciences, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany; Scherf, Katharina Anne; Department of Bioactive and Functional Food Chemistry, Institute of Applied Biosciences, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany; Popper, Lutz; Mühlenchemie GmbH & Co. KG, Ahrensburg, Germany; Hitzmann, Bernd; Department of Process Analytics, Institute of Food Science and Biotechnology, University of Hohenheim, Stuttgart, Germany
    Background and Objectives: Spectroscopy of wheat kernels and flour has been used as a rapid tool to assess wheat quality, but predictions still lack in accuracy for most quality parameters except for protein content. To enable an improved prediction of further quality characteristics, new approaches are needed. This study investigates if the preprocessing of flour into flour fractions (by air classification, sieving) or dough and subsequent spectroscopic analysis of these types of samples could be a new way to improve wheat quality predictions. For this purpose, spectral differences are investigated and predictions of farinograph parameters are compared for fluorescence spectra of flour, flour fractions, and dough. Findings: A wide variety of fluorophores present in cereal products was identified. Their peak intensities significantly differed for flour, flour fractions, and dough. Flour and sieve fractions were superior in predicting water absorption (R2CV flour = 0.79; R2CV 32–50 µm = 0.81), while gluten and dough samples strongly improved predictions of rheological properties, especially dough development time (R2CV flour = 0.64; R2CV dough = 0.90; R2CV gluten = 0.84). Conclusion: Preprocessing of flour samples greatly alters their composition (e.g., protein enrichment), which is also reflected by spectral differences. Spectra of different sample types therefore contain different information and have the potential to improve the prediction of wheat quality. Significance and Novelty: This is the first study that investigates spectral differences of a large number of different flour fractions and dough using fluorescence spectroscopy and subsequently underlines the potential of this novel approach to improve wheat quality prediction in the future.
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    Spectroscopy‐based prediction of 73 wheat quality parameters and insights for practical applications
    (2023) Nagel‐Held, Johannes; El Hassouni, Khaoula; Longin, Friedrich; Hitzmann, Bernd
    Background and Objectives: Quality assessment of bread wheat is time-consuming and requires the determination of many complex characteristics. Because of its simplicity, protein content prediction using near-infrared spectroscopy (NIRS) serves as the primary quality attribute in wheat trade. To enable the prediction of more complex traits, information from Raman and fluorescence spectra is added to the NIR spectra of whole grain and extracted flour. Model robustness is assessed by predictions across cultivars, locations, and years. The prediction error is corrected for the measurement error of the reference methods. Findings: Successful prediction, robustness testing, and measurement error correction were achieved for several parameters. Predicting loaf volume yielded a corrected prediction error RMSECV of 27.5 mL/100 g flour and an R² of 0.86. However, model robustness was limited due to data distribution, environmental factors, and temporal influences. Conclusions: The proposed method was proven to be suitable for applications in the wheat value chain. Furthermore, the study provides valuable insights for practical implementations. Significance and Novelty With up to 1200 wheat samples, this is the largest study on predicting complex characteristics comprising agronomic traits; dough rheological parameters measured by Extensograph, micro-doughLAB, and GlutoPeak; baking trial parameters like loaf volume; and specific ingredients, such as grain protein content, sugars, and minerals.