Unveiling the potential: Harnessing spectral technologies for enhanced protein and gluten content prediction in wheat grains and flour
Year: 2025
Type: article
Abstract: Protein and gluten content is one of the most crucial quality characteristics in the wheat industry. However, these properties are measured after grinding wheat kernels into the flour. In this study, grain samples from 38 different wheat cultivars were collected, and their protein, wet and dry gluten content were measured traditionally. Spectral information was obtained using three non-destructive instruments, including benchtop visible-near infrared hyperspectral imaging (HSI), portable short wavelength infrared HSI and Fourier-Transform near-infrared spectroscopy from both whole grains and their flour samples. Partial least squares regression (PLSR) and Gaussian process regression (GPR) with three spectral pre-treatments were used to compare performances and Neighborhood Component Analysis was applied for wavelength selection. Through HSI, wheat kernels revealed their protein and gluten content with remarkable precision, achieving R 2 P values exceeding 0.97 using GPR based on whole kernel data utilising four wavelengths in the Visible range. The key novelty of this work is that it demonstrates the suitability of visible range hyperspectral imaging for direct prediction of protein and gluten with high accuracy, without the need for sample grinding, thus underscoring the significance of visible spectral information in determining protein and gluten-related parameters. • Models based on whole kernels perform better than models based on flour data. • Gaussian Process Regression performs better than Partial Least Squares Regression. • Visible region is as efficient as broader regions such as NIR and SWIR. • WG, DG and protein can be predicted by four wavelengths within the Vis range.
Source: Current Research in Food Science
Authors: Gözde Özdoğan, Aoife Gowen
Institution: University College Dublin
Language: English
Subfield: Analytical Chemistry
Field: Chemistry
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $3,170
Funder: Milli Eğitim Bakanliği
OpenAlex ID: W4409385093