Prediction of wheat gluten composition via near-infrared spectroscopy

Clemens Schuster; Julien Huen; Katharina Anne Scherf

doi:10.1016/j.crfs.2023.100471

Prediction of wheat gluten composition via near-infrared spectroscopy

Curr Res Food Sci. 2023 Mar 2:6:100471. doi: 10.1016/j.crfs.2023.100471. eCollection 2023.

Authors

Clemens Schuster¹, Julien Huen², Katharina Anne Scherf^{1

3}

Affiliations

¹ Leibniz-Institute for Food Systems Biology at the Technical University of Munich, Lise-Meitner-Str. 34, 85354, Freising, Germany.
² Technologie-Transfer-Zentrum ttz Bremerhaven, Am Lunedeich 12, 27572, Bremerhaven, Germany.
³ Department of Bioactive and Functional Food Chemistry, Institute of Applied Biosciences, Karlsruhe Institute of Technology (KIT), Adenauerring 20a, 76131, Karlsruhe, Germany.

Abstract

Gluten composition is an important quality parameter for wheat flour, because it is strongly correlated to baking quality. Wheat proteins are commonly extracted stepwise and analysed using RP-HPLC-UV to determine the gluten composition. This procedure is very time-consuming and labour-intensive. Therefore, a new, fast and easy method to quantitate gluten proteins was established using NIR spectroscopy (NIRS). PLS-regression models were calculated containing 207 samples for calibration and 169 for test set validation. Albumin/globulin (ALGL), gluten, gliadin and glutenin content was predicted with a root mean square error of prediction (RMSEP) of 2.01 mg/g, 6.09 mg/g, 4.25 mg/g and 3.50 mg/g, respectively. High-molecular-weight glutenin subunits (HMW-GS) and low-molecular-weight glutenin subunits (LMW-GS) were predicted with a RMSEP of 1.12 mg/g and 2.38 mg/g. The relative error was too high for ALGL, LMW-GS and HMW-GS, but that of gluten, gliadins and glutenins was in a range comparable to the reference method. Therefore, the new NIRS method can be used to estimate the gluten composition of wheat flour, including the gliadin/glutenin and the LMW-GS/HMW-GS ratio.

Keywords: Baking quality; Gliadin; Glutenin; NIRS; PLS-regression; Reversed-phase high-performance liquid chromatography (RP-HPLC).